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Articles 2491 - 2520 of 3560

Full-Text Articles in Databases and Information Systems

Impact Of Multimedia In Sina Weibo: Popularity And Life Span, Xun Zhao, Feida Zhu, Weining Qian, Aoying Zhou Nov 2012

Impact Of Multimedia In Sina Weibo: Popularity And Life Span, Xun Zhao, Feida Zhu, Weining Qian, Aoying Zhou

Research Collection School Of Computing and Information Systems

Multimedia contents such as images and videos are widely used in social network sites nowadays. Sina Weibo, a Chinese microblogging service, is one of the first microblog platforms to incorporate multimedia content sharing features. This work provides statistical analysis on how multimedia contents are produced, consumed, and propagated in Sina Weibo. Based on 230 million tweets and 1.8 million user profiles in Sina Weibo, we study the impact of multimedia contents on the popularity of both users and tweets as well as tweet life span. Our preliminary study shows that multimedia tweets dominant pure text ones in SinaWeibo. Multimedia contents …

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Divad: A Dynamic And Interactive Visual Analytical Dashboard For Exploring And Analyzing Transport Data, Tin Seong Kam, Ketan Barshikar, Shaun Jun Hua Tan Nov 2012

Divad: A Dynamic And Interactive Visual Analytical Dashboard For Exploring And Analyzing Transport Data, Tin Seong Kam, Ketan Barshikar, Shaun Jun Hua Tan

Research Collection School Of Computing and Information Systems

The advances in location-based data collection technologies such as GPS, RFID etc. and the rapid reduction of their costs provide us with a huge and continuously increasing amount of data about movement of vehicles, people and goods in an urban area. This explosive growth of geospatially-referenced data has far outpaced the planner’s ability to utilize and transform the data into insightful information thus creating an adverse impact on the return on the investment made to collect and manage this data. Addressing this pressing need, we designed and developed DIVAD, a dynamic and interactive visual analytics dashboard to allow city planners …

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Multiview Semi-Supervised Learning With Consensus, Guangxia Li, Kuiyu Chang, Steven C. H. Hoi Nov 2012

Multiview Semi-Supervised Learning With Consensus, Guangxia Li, Kuiyu Chang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Obtaining high-quality and up-to-date labeled data can be difficult in many real-world machine learning applications. Semi-supervised learning aims to improve the performance of a classifier trained with limited number of labeled data by utilizing the unlabeled ones. This paper demonstrates a way to improve the transductive SVM, which is an existing semi-supervised learning algorithm, by employing a multiview learning paradigm. Multiview learning is based on the fact that for some problems, there may exist multiple perspectives, so called views, of each data sample. For example, in text classification, the typical view contains a large number of raw content features such …

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A Unified Learning Framework For Auto Face Annotation By Mining Web Facial Images, Dayong Wang, Steven C. H. Hoi, Ying He Nov 2012

A Unified Learning Framework For Auto Face Annotation By Mining Web Facial Images, Dayong Wang, Steven C. H. Hoi, Ying He

Research Collection School Of Computing and Information Systems

Auto face annotation plays an important role in many real-world multimedia information and knowledge management systems. Recently there is a surge of research interests in mining weakly-labeled facial images on the internet to tackle this long-standing research challenge in computer vision and image understanding. In this paper, we present a novel unified learning framework for face annotation by mining weakly labeled web facial images through interdisciplinary efforts of combining sparse feature representation, content-based image retrieval, transductive learning and inductive learning techniques. In particular, we first introduce a new search-based face annotation paradigm using transductive learning, and then propose an effective …

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A Generalized Cluster Centroid Based Classifier For Text Categorization, Guansong Pang, Shengyi Jiang Nov 2012

A Generalized Cluster Centroid Based Classifier For Text Categorization, Guansong Pang, Shengyi Jiang

Research Collection School Of Computing and Information Systems

In this paper, a Generalized Cluster Centroid based Classifier (GCCC) and its variants for text categorization are proposed by utilizing a clustering algorithm to integrate two wellknown classifiers, i.e., the K-nearest-neighbor (KNN) classifier and the Rocchio classifier. KNN, a lazy learning method, suffers from inefficiency in online categorization while achieving remarkable effectiveness. Rocchio, which has efficient categorization performance, fails to obtain an expressive categorization model due to its inherent linear separability assumption. Our proposed method mainly focuses on two points: one point is that we use a clustering algorithm to strengthen the expressiveness of the Rocchio model; another one is …

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Understanding Engagement In Educational Computer Games, Fiona Fui-Hoon Nah, Yunjie Zhou, Adeline Boey, Hanji Li Oct 2012

Understanding Engagement In Educational Computer Games, Fiona Fui-Hoon Nah, Yunjie Zhou, Adeline Boey, Hanji Li

Research Collection School Of Computing and Information Systems

This paper presents an empirical study to understand engagement in educational computer games. Engagement is defined as an experience that occupies an individual’s attention and captures one’s interest. The nature of engagement is viewed as comprising conditions, actions, and outcomes of engagement. The data collection method includes in-depth interviews with 12 educational computer game players who have experienced engagement in playing these games. We used the Grounded Theory (GT) approach to develop an understanding of user engagement in the computer game-based learning context.

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Talk Versus Work: Characteristics Of Developer Collaboration On The Jazz Platform, Subhajit Datta, Renuka Sindhgatta, Bikram Sengupta Oct 2012

Talk Versus Work: Characteristics Of Developer Collaboration On The Jazz Platform, Subhajit Datta, Renuka Sindhgatta, Bikram Sengupta

Research Collection School Of Computing and Information Systems

IBM's Jazz initiative offers a state-of-the-art collaborative development environment (CDE) facilitating developer interactions around interdependent units of work. In this paper, we analyze development data across two versions of a major IBM product developed on the Jazz platform, covering in total 19 months of development activity, including 17,000+ work items and 61,000+ comments made by more than 190 developers in 35 locations. By examining the relation between developer talk and work, we find evidence that developers maintain a reasonably high level of connectivity with peer developers with whom they share work dependencies, but the span of a developer's communication goes …

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The Shanghai-Hongkong Team At Mediaeval2012: Violent Scene Detection Using Trajectory-Based Features, Yu-Gang Jiang, Qi Dai, Chun Chet Tan, Xiangyang Xue, Chong-Wah Ngo Oct 2012

The Shanghai-Hongkong Team At Mediaeval2012: Violent Scene Detection Using Trajectory-Based Features, Yu-Gang Jiang, Qi Dai, Chun Chet Tan, Xiangyang Xue, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

The Violent Scene Detection task offers a very practical challenge in detecting complex and diverse violent video clips in movies. In this working note paper, we will briefly describe our system and discuss the results, which achieved top performance in mAP@201 and runner-up in mAP@100, among all 35 submissions worldwide. The central component of our system is a set of features derived from the appearance and motion of local patch trajectories [2]. We use these features and SVM classifier as the baseline approach and add in a few other components to further improve the performance. Our findings indicate that the …

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Trajectory-Based Modeling Of Human Actions With Motion Reference Points, Yu-Gang Jiang, Qi Dai, Xiangyang Xue, Wei Liu, Chong-Wah Ngo Oct 2012

Trajectory-Based Modeling Of Human Actions With Motion Reference Points, Yu-Gang Jiang, Qi Dai, Xiangyang Xue, Wei Liu, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Human action recognition in videos is a challenging problem with wide applications. State-of-the-art approaches often adopt the popular bag-of-features representation based on isolated local patches or temporal patch trajectories, where motion patterns like object relationships are mostly discarded. This paper proposes a simple representation specifically aimed at the modeling of such motion relationships. We adopt global and local reference points to characterize motion information, so that the final representation can be robust to camera movement. Our approach operates on top of visual codewords derived from local patch trajectories, and therefore does not require accurate foreground-background separation, which is typically a …

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Neural Modeling Of Episodic Memory: Encoding, Retrieval, And Forgetting, Wenwen Wang, Budhitama Subagdja, Ah-Hwee Tan, Janusz A. Starzyk Oct 2012

Neural Modeling Of Episodic Memory: Encoding, Retrieval, And Forgetting, Wenwen Wang, Budhitama Subagdja, Ah-Hwee Tan, Janusz A. Starzyk

Research Collection School Of Computing and Information Systems

This paper presents a neural model that learns episodic traces in response to a continuous stream of sensory input and feedback received from the environment. The proposed model, based on fusion Adaptive Resonance Theory (fusion ART) network, extracts key events and encodes spatio-temporal relations between events by creating cognitive nodes dynamically. The model further incorporates a novel memory search procedure, which performs parallel search of stored episodic traces continuously. Combined with a mechanism of gradual forgetting, the model is able to achieve a high level of memory performance and robustness, while controlling memory consumption over time. We present experimental studies, …

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A Probabilistic Graphical Model For Topic And Preference Discovery On Social Media, Lu Liu, Feida Zhu, Lei Zhang, Shiqiang Yang Oct 2012

A Probabilistic Graphical Model For Topic And Preference Discovery On Social Media, Lu Liu, Feida Zhu, Lei Zhang, Shiqiang Yang

Research Collection School Of Computing and Information Systems

Many web applications today thrive on offering services for large-scale multimedia data, e.g., Flickr for photos and YouTube for videos. However, these data, while rich in content, are usually sparse in textual descriptive information. For example, a video clip is often associated with only a few tags. Moreover, the textual descriptions are often overly specific to the video content. Such characteristics make it very challenging to discover topics at a satisfactory granularity on this kind of data. In this paper, we propose a generative probabilistic model named Preference-Topic Model (PTM) to introduce the dimension of user preferences to enhance the …

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Influentials, Novelty, And Social Contagion: The Viral Power Of Average Friends, Close Communities, And Old News, Nicholas Harrigan, Palakorn Achananuparp, Ee Peng Lim Oct 2012

Influentials, Novelty, And Social Contagion: The Viral Power Of Average Friends, Close Communities, And Old News, Nicholas Harrigan, Palakorn Achananuparp, Ee Peng Lim

Research Collection School Of Computing and Information Systems

What is the effect of (1) popular individuals, and (2) community structures on the retransmission of socially contagious behavior? We examine a community of Twitter users over a five month period, operationalizing social contagion as ‘retweeting’, and social structure as the count of subgraphs (small patterns of ties and nodes) between users in the follower/following network. We find that popular individuals act as ‘inefficient hubs’ for social contagion: they have limited attention, are overloaded with inputs, and therefore display limited responsiveness to viral messages. We argue this contradicts the ‘law of the few’ and ‘influentials hypothesis’. We find that community …

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Entity Synonyms For Structured Web Search, Tao Cheng, Hady W. Lauw, Stelios Paparizos Oct 2012

Entity Synonyms For Structured Web Search, Tao Cheng, Hady W. Lauw, Stelios Paparizos

Research Collection School Of Computing and Information Systems

Nowadays, there are many queries issued to search engines targeting at finding values from structured data (e.g., movie showtime of a specific location). In such scenarios, there is often a mismatch between the values of structured data (how content creators describe entities) and the web queries (how different users try to retrieve them). Therefore, recognizing the alternative ways people use to reference an entity, is crucial for structured web search. In this paper, we study the problem of automatic generation of entity synonyms over structured data toward closing the gap between users and structured data. We propose an offline, data-driven …

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In-Game Action List Segmentation And Labeling In Real-Time Strategy Games, Wei Gong, Ee-Peng Lim, Palakorn Achananuparp, Feida Zhu, David Lo, Freddy Chong-Tat Chua Sep 2012

In-Game Action List Segmentation And Labeling In Real-Time Strategy Games, Wei Gong, Ee-Peng Lim, Palakorn Achananuparp, Feida Zhu, David Lo, Freddy Chong-Tat Chua

Research Collection School Of Computing and Information Systems

In-game actions of real-time strategy (RTS) games are extremely useful in determining the players' strategies, analyzing their behaviors and recommending ways to improve their play skills. Unfortunately, unstructured sequences of in-game actions are hardly informative enough for these analyses. The inconsistency we observed in human annotation of in-game data makes the analytical task even more challenging. In this paper, we propose an integrated system for in-game action segmentation and semantic label assignment based on a Conditional Random Fields (CRFs) model with essential features extracted from the in-game actions. Our experiments demonstrate that the accuracy of our solution can be as …

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Guest Editors’ Introduction: Methods Innovations For The Empirical Study Of Technology Adoption And Diffusion, Robert John Kauffman, Angsana A. Techatassanasoontorn Sep 2012

Guest Editors’ Introduction: Methods Innovations For The Empirical Study Of Technology Adoption And Diffusion, Robert John Kauffman, Angsana A. Techatassanasoontorn

Research Collection School Of Computing and Information Systems

The literature on technology adoption and diffusion is ahighly mature area of Information Systems (IS) research,which requires a deft hand in research to support the creationof new contributions of knowledge. In this specialissue, we focus on the application of various methods,including new ones, to shed light on research questions thathave not been understood fully in prior research. In particular,we will showcase research that involves theapplication of event history analysis and spatial econometrics,as well as count data models to study frequencyrelatedphenomena for changes and development in technologyadoption and diffusion. We also include an articlethat employs game theory, as well as another …

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Bduol: Double Updating Online Learning On A Fixed Budget, Peilin Zhao, Steven C. H. Hoi Sep 2012

Bduol: Double Updating Online Learning On A Fixed Budget, Peilin Zhao, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Kernel-based online learning often exhibits promising empirical performance for various applications according to previous studies. However, it often suffers a main shortcoming, that is, the unbounded number of support vectors, making it unsuitable for handling large-scale datasets. In this paper, we investigate the problem of budget kernel-based online learning that aims to constrain the number of support vectors by a predefined budget when learning the kernel-based prediction function in the online learning process. Unlike the existing studies, we present a new framework of budget kernel-based online learning based on a recently proposed online learning method called “Double Updating Online Learning” …

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(Hidden) Social Influences In Switching Mobile Service Platforms, Virpi K. Tuunainen, Tuure Tuunanen, Fiona Fui-Hoon Nah Aug 2012

(Hidden) Social Influences In Switching Mobile Service Platforms, Virpi K. Tuunainen, Tuure Tuunanen, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

During the past few years, the mobile industry has gone through a radical change from business focusing on excellence in device manufacturing and supply chain management to ecosystems around successful focal players, such as Apple and Google, controlling these service platforms. In order to compete in this environment, these firms need to understand what makes a consumer switch between these mobile service platforms. To that end, we conducted an inductive qualitative study with university students from Finland and USA as subjects (142 altogether), delving into how and why consumers switch mobile phones, and what are the factors affecting their decisions. …

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A Non-Parametric Visual-Sense Model Of Images: Extending The Cluster Hypothesis Beyond Text, Kong-Wah Wan, Ah-Hwee Tan, Joo-Hwee Lim, Liang-Tien Chia Aug 2012

A Non-Parametric Visual-Sense Model Of Images: Extending The Cluster Hypothesis Beyond Text, Kong-Wah Wan, Ah-Hwee Tan, Joo-Hwee Lim, Liang-Tien Chia

Research Collection School Of Computing and Information Systems

The main challenge of a search engine is to find information that are relevant and appropriate. However, this can become difficult when queries are issued using ambiguous words. Rijsbergen first hypothesized a clustering approach for web pages wherein closely associated pages are treated as a semantic group with the same relevance to the query (Rijsbergen 1979). In this paper, we extend Rijsbergen’s cluster hypothesis to multimedia content such as images. Given a user query, the polysemy in the return image set is related to the many possible meanings of the query. We develop a method to cluster the polysemous images …

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Collective Churn Prediction In Social Network, Richard J. Oentaryo, Ee-Peng Lim, David Lo, Feida Zhu, Philips K. Prasetyo Aug 2012

Collective Churn Prediction In Social Network, Richard J. Oentaryo, Ee-Peng Lim, David Lo, Feida Zhu, Philips K. Prasetyo

Research Collection School Of Computing and Information Systems

In service-based industries, churn poses a significant threat to the integrity of the user communities and profitability of the service providers. As such, research on churn prediction methods has been actively pursued, involving either intrinsic, user profile factors or extrinsic, social factors. However, existing approaches often address each type of factors separately, thus lacking a comprehensive view of churn behaviors. In this paper, we propose a new churn prediction approach based on collective classification (CC), which accounts for both the intrinsic and extrinsic factors by utilizing the local features of, and dependencies among, individuals during prediction steps. We evaluate our …

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Presynaptic Learning And Memory With A Persistent Firing Neuron And A Habituating Synapse: A Model Of Short Term Persistent Habituation, Kiruthika Ramanathan, Ning Ning, Dhiviya Dhanasekar, Guoqi Li, Luping Shi, Prahlad Vadakkepat Aug 2012

Presynaptic Learning And Memory With A Persistent Firing Neuron And A Habituating Synapse: A Model Of Short Term Persistent Habituation, Kiruthika Ramanathan, Ning Ning, Dhiviya Dhanasekar, Guoqi Li, Luping Shi, Prahlad Vadakkepat

Research Collection School Of Computing and Information Systems

Our paper explores the interaction of persistent firing axonal and presynaptic processes in the generation of short term memory for habituation. We first propose a model of a sensory neuron whose axon is able to switch between passive conduction and persistent firing states, thereby triggering short term retention to the stimulus. Then we propose a model of a habituating synapse and explore all nine of the behavioral characteristics of short term habituation in a two neuron circuit. We couple the persistent firing neuron to the habituation synapse and investigate the behavior of short term retention of habituating response. Simulations show …

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Shortest Path Computation With No Information Leakage, Kyriakos Mouratidis, Man Lung Yiu Aug 2012

Shortest Path Computation With No Information Leakage, Kyriakos Mouratidis, Man Lung Yiu

Research Collection School Of Computing and Information Systems

Shortest path computation is one of the most common queries in location-based services (LBSs). Although particularly useful, such queries raise serious privacy concerns. Exposing to a (potentially untrusted) LBS the client’s position and her destination may reveal personal information, such as social habits, health condition, shopping preferences, lifestyle choices, etc. The only existing method for privacy-preserving shortest path computation follows the obfuscation paradigm; it prevents the LBS from inferring the source and destination of the query with a probability higher than a threshold. This implies, however, that the LBS still deduces some information (albeit not exact) about the client’s location …

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A Secure And Efficient Discovery Service System In Epcglobal Network, Jie Shi, Yingjiu Li, Robert H. Deng Aug 2012

A Secure And Efficient Discovery Service System In Epcglobal Network, Jie Shi, Yingjiu Li, Robert H. Deng

Research Collection School Of Computing and Information Systems

In recent years, the Internet of Things (IOT) has drawn considerable attention from the industrial and research communities. Due to the vast amount of data generated through IOT devices and users, there is an urgent need for an effective search engine to help us make sense of this massive amount of data. With this motivation, we begin our initial works on developing a secure and efficient search engine (SecDS) based on EPC Discovery Services (EPCDS) for EPCglobal network, an integral part of IOT. SecDS is designed to provide a bridge between different partners of supply chains to share information while …

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Modeling Concept Dynamics For Large Scale Music Search, Jialie Shen, Hwee Hwa Pang, Meng Wang, Shuicheng Yan Aug 2012

Modeling Concept Dynamics For Large Scale Music Search, Jialie Shen, Hwee Hwa Pang, Meng Wang, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Continuing advances in data storage and communication technologies have led to an explosive growth in digital music collections. To cope with their increasing scale, we need effective Music Information Retrieval (MIR) capabilities like tagging, concept search and clustering. Integral to MIR is a framework for modelling music documents and generating discriminative signatures for them. In this paper, we introduce a multimodal, layered learning framework called DMCM. Distinguished from the existing approaches that encode music as an ensemble of order-less feature vectors, our framework extracts from each music document a variety of acoustic features, and translates them into low-level encodings over …

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Confidence-Aware Graph Regularization With Heterogeneous Pairwise Features, Yuan Fang, Bo-June Paul Hsu, Kevin Chen-Chuan Chang Aug 2012

Confidence-Aware Graph Regularization With Heterogeneous Pairwise Features, Yuan Fang, Bo-June Paul Hsu, Kevin Chen-Chuan Chang

Research Collection School Of Computing and Information Systems

Conventional classification methods tend to focus on features of individual objects, while missing out on potentially valuable pairwise features that capture the relationships between objects. Although recent developments on graph regularization exploit this aspect, existing works generally assume only a single kind of pairwise feature, which is often insufficient. We observe that multiple, heterogeneous pairwise features can often complement each other and are generally more robust in modeling the relationships between objects. Furthermore, as some objects are easier to classify than others, objects with higher initial classification confidence should be weighed more towards classifying related but more ambiguous objects, an …

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Boosting Multi-Kernel Locality-Sensitive Hashing For Scalable Image Retrieval, Hao Xia, Steven C. H. Hoi, Pengcheng Wu, Rong Jin Aug 2012

Boosting Multi-Kernel Locality-Sensitive Hashing For Scalable Image Retrieval, Hao Xia, Steven C. H. Hoi, Pengcheng Wu, Rong Jin

Research Collection School Of Computing and Information Systems

Similarity search is a key challenge for multimedia retrieval applications where data are usually represented in high-dimensional space. Among various algorithms proposed for similarity search in high-dimensional space, Locality-Sensitive Hashing (LSH) is the most popular one, which recently has been extended to Kernelized Locality-Sensitive Hashing (KLSH) by exploiting kernel similarity for better retrieval efficacy. Typically, KLSH works only with a single kernel, which is often limited in real-world multimedia applications, where data may originate from multiple resources or can be represented in several different forms. For example, in content-based multimedia retrieval, a variety of features can be extracted to represent …

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Online Feature Selection For Mining Big Data, Steven C. H. Hoi, Jialei Wang, Peilin Zhao, Rong Jin Aug 2012

Online Feature Selection For Mining Big Data, Steven C. H. Hoi, Jialei Wang, Peilin Zhao, Rong Jin

Research Collection School Of Computing and Information Systems

Most studies of online learning require accessing all the attributes/features of training instances. Such a classical setting is not always appropriate for real-world applications when data instances are of high dimensionality or the access to it is expensive to acquire the full set of attributes/features. To address this limitation, we investigate the problem of Online Feature Selection (OFS) in which the online learner is only allowed to maintain a classifier involved a small and fixed number of features. The key challenge of Online Feature Selection is how to make accurate prediction using a small and fixed number of active features. …

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Embracing Analytics For A Better Competitive Edge, Tin Seong Kam Jul 2012

Embracing Analytics For A Better Competitive Edge, Tin Seong Kam

Research Collection School Of Computing and Information Systems

No abstract provided.

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Formal Analysis Of Pervasive Computing Systems, Yan Liu, Xian Zhang, Jin Song Dong, Yang Liu, Jun Sun, Jit Biswas, Mounir Mokhtari Jul 2012

Formal Analysis Of Pervasive Computing Systems, Yan Liu, Xian Zhang, Jin Song Dong, Yang Liu, Jun Sun, Jit Biswas, Mounir Mokhtari

Research Collection School Of Computing and Information Systems

Pervasive computing systems are heterogenous and complex as they usually involve human activities, various sensors and actuators as well as middleware for system controlling. Therefore, analyzing such systems is highly nontrivial. In this work, we propose to use formal methods for analyzing pervasive computing systems. Firstly, a formal modeling framework is proposed to cover the main characteristics of pervasive computing systems (e.g., context-awareness, concurrent communications, layered architectures). Secondly, we identify the safety requirements (e.g., free of deadlocks and conflicts etc.) and propose their specifications as safety and liveness properties. Finally, we demonstrate our ideas using a case study of a …

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Adaptive Cgf For Pilots Training In Air Combat Simulation, Teck-Hou Teng, Ah-Hwee Tan, Wee-Sze Ong, Kien-Lip Lee Jul 2012

Adaptive Cgf For Pilots Training In Air Combat Simulation, Teck-Hou Teng, Ah-Hwee Tan, Wee-Sze Ong, Kien-Lip Lee

Research Collection School Of Computing and Information Systems

Training of combat fighter pilots is often conducted using either human opponents or non-adaptive computer-generated force (CGF) inserted with the doctrine for conducting air combat mission. The novelty and challenges of such non-adaptive doctrine-driven CGF is often lost quickly. Incorporating more complex knowledge manually is known to be tedious and time-consuming. Therefore, a study of using adaptive CGF to learn from the real-time interactions with human pilots to extend the existing doctrine is conducted in this work. The goal of this study is to show how an adaptive CGF can be more effective than a non-adaptive doctrine-driven CGF for simulator-based …

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Information-Theoretic Multi-View Domain Adaptation, Pei Yang, Wei Gao, Qi Tan, Kam-Fai Wong Jul 2012

Information-Theoretic Multi-View Domain Adaptation, Pei Yang, Wei Gao, Qi Tan, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

We use multiple views for cross-domain document classification. The main idea is to strengthen the views’ consistency for target data with source training data by identifying the correlations of domain-specific features from different domains. We present an Information-theoretic Multi-view Adaptation Model (IMAM) based on a multi-way clustering scheme, where word and link clusters can draw together seemingly unrelated domain-specific features from both sides and iteratively boost the consistency between document clusterings based on word and link views. Experiments show that IMAM significantly outperforms state-of-the-art baselines.

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