Scalable Online Kernel Learning,
2017
Singapore Management University
Scalable Online Kernel Learning, Jing Lu
Dissertations and Theses Collection (Open Access)
One critical deficiency of traditional online kernel learning methods is their increasing and unbounded number of support vectors (SV’s), making them inefficient and non-scalable for large-scale applications. Recent studies on budget online learning have attempted to overcome this shortcoming by bounding the number of SV’s. Despite being extensively studied, budget algorithms usually suffer from several drawbacks.
First of all, although existing algorithms attempt to bound the number of SV’s at each iteration, most of them fail to bound the number of SV’s for the final averaged classifier, which is commonly used for online-to-batch conversion. To solve this problem, we propose …
An Integrated Framework For Modeling And Predicting Spatiotemporal Phenomena In Urban Environments,
2017
Singapore Management University
An Integrated Framework For Modeling And Predicting Spatiotemporal Phenomena In Urban Environments, Tuc Viet Le
Dissertations and Theses Collection (Open Access)
This thesis proposes a general solution framework that integrates methods in machine learning in creative ways to solve a diverse set of problems arising in urban environments. It particularly focuses on modeling spatiotemporal data for the purpose of predicting urban phenomena. Concretely, the framework is applied to solve three specific real-world problems: human mobility prediction, trac speed prediction and incident prediction. For human mobility prediction, I use visitor trajectories collected a large theme park in Singapore as a simplified microcosm of an urban area. A trajectory is an ordered sequence of attraction visits and corresponding timestamps produced by a visitor. …
Sourcevote: Fusing Multi-Valued Data Via Inter-Source Agreements,
2017
Singapore Management University
Sourcevote: Fusing Multi-Valued Data Via Inter-Source Agreements, Xiu Susie Fang, Quan Z. Sheng, Xianzhi Wang, Mahmoud Barhamgi, Lina Yao, Anne H.H. Ngu
Research Collection School Of Computing and Information Systems
Data fusion is a fundamental research problem of identifyingtrue values of data items of interest from conflicting multi-sourceddata. Although considerable research efforts have been conducted on thistopic, existing approaches generally assume every data item has exactlyone true value, which fails to reflect the real world where data items withmultiple true values widely exist. In this paper, we propose a novel approach,SourceVote, to estimate value veracity for multi-valued data items.SourceVote models the endorsement relations among sources by quantifyingtheir two-sided inter-source agreements. In particular, two graphs areconstructed to model inter-source relations. Then two aspects of sourcereliability are derived from these graphs and …
Semvis: Semantic Visualization For Interactive Topical Analysis,
2017
Singapore Management University
Semvis: Semantic Visualization For Interactive Topical Analysis, Le Van Minh Tuan, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
Exploratory analysis of a text corpus is an important task that can be aided by informative visualization. One spatially-oriented form of document visualization is a scatterplot, whereby every document is associated with a coordinate, and relationships among documents can be perceived through their spatial distances. Semantic visualization further infuses the visualization space with latent semantics, by incorporating a topic model that has a representation in the visualization space, allowing users to also perceive relationships between documents and topics spatially. We illustrate how a semantic visualization system called SemVis could be used to navigate a text corpus interactively and topically via …
Collaborative Topic Regression With Denoising Autoencoder For Content And Community Co-Representation,
2017
Singapore Management University
Collaborative Topic Regression With Denoising Autoencoder For Content And Community Co-Representation, Trong T. Nguyen, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Personalized recommendation of items frequently faces scenarios where we have sparse observations on users' adoption of items. In the literature, there are two promising directions. One is to connect sparse items through similarity in content. The other is to connect sparse users through similarity in social relations. We seek to integrate both types of information, in addition to the adoption information, within a single integrated model. Our proposed method models item content via a topic model, and user communities via an autoencoder model, while bridging a user's community-based preference to her topic-based preference. Experiments on public real-life data showcase the …
Indexable Bayesian Personalized Ranking For Efficient Top-K Recommendation,
2017
Singapore Management University
Indexable Bayesian Personalized Ranking For Efficient Top-K Recommendation, Dung D. Le, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Top-k recommendation seeks to deliver a personalized recommendation list of k items to a user. The dual objectives are (1) accuracy in identifying the items a user is likely to prefer, and (2) efficiency in constructing the recommendation list in real time. One direction towards retrieval efficiency is to formulate retrieval as approximate k nearest neighbor (kNN) search aided by indexing schemes, such as locality-sensitive hashing, spatial trees, and inverted index. These schemes, applied on the output representations of recommendation algorithms, speed up the retrieval process by automatically discarding a large number of potentially irrelevant items when given a user …
Answerbot: Automated Generation Of Answer Summary To Developers’ Technical Questions,
2017
Singapore Management University
Answerbot: Automated Generation Of Answer Summary To Developers’ Technical Questions, Bowen Xu, Zhenchang Xing, Xin Xia, David Lo
Research Collection School Of Computing and Information Systems
The prevalence of questions and answers on domain-specific Q&A sites like Stack Overflow constitutes a core knowledge asset for software engineering domain. Although search engines can return a list of questions relevant to a user query of some technical question, the abundance of relevant posts and the sheer amount of information in them makes it difficult for developers to digest them and find the most needed answers to their questions. In this work, we aim to help developers who want to quickly capture the key points of several answer posts relevant to a technical question before they read the details …
Sourcevote: Fusing Multi-Valued Data Via Inter-Source Agreements,
2017
Macquarie University
Sourcevote: Fusing Multi-Valued Data Via Inter-Source Agreements, Xiu Susie Fang, Quan Z. Sheng, Xianzhi Wang, Mahmoud Barhamgi, Lina Yao, Anne H.H. Ngu
Research Collection School Of Computing and Information Systems
Data fusion is a fundamental research problem of identifying true values of data items of interest from conflicting multi-sourced data. Although considerable research efforts have been conducted on this topic, existing approaches generally assume every data item has exactly one true value, which fails to reflect the real world where data items with multiple true values widely exist. In this paper, we propose a novel approach,SourceVote, to estimate value veracity for multi-valued data items. SourceVote models the endorsement relations among sources by quantifying their two-sided inter-source agreements. In particular, two graphs are constructed to model inter-source relations. Then two aspects …
Tweet Geolocation: Leveraging Location, User And Peer Signals,
2017
Singapore Management University
Tweet Geolocation: Leveraging Location, User And Peer Signals, Wen-Haw Chong, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Which venue is a tweet posted from? We referred this as fine-grained geolocation. To solve this problem effectively, we develop novel techniques to exploit each posting user's content history. This is motivated by our finding that most users do not share their visitation history, but have ample content history from tweet posts. We formulate fine-grained geolocation as a ranking problem whereby given a test tweet, we rank candidate venues. We propose several models that leverage on three types of signals from locations, users and peers. Firstly, the location signals are words that are indicative of venues. We propose a location-indicative …
Modeling Check-In Behavior With Geographical Neighborhood Influence Of Venues,
2017
Singapore Management University
Modeling Check-In Behavior With Geographical Neighborhood Influence Of Venues, Thanh Nam Doan, Ee Peng Lim
Research Collection School Of Computing and Information Systems
With many users adopting location-based social networks (LBSNs) to share their daily activities, LBSNs become a gold mine for researchers to study human check-in behavior. Modeling such behavior can benefit many useful applications such as urban planning and location-aware recommender systems. Unlike previous studies [4,6,12,17] that focus on the effect of distance on users checking in venues, we consider two venue-specific effects of geographical neighborhood influence, namely, spatial homophily and neighborhood competition. The former refers to the fact that venues share more common features with their spatial neighbors, while the latter captures the rivalry of a venue and its nearby …
On Analyzing Job Hop Behavior And Talent Flow Networks,
2017
McLaren Applied Technologies
On Analyzing Job Hop Behavior And Talent Flow Networks, Richard J. Oentaryo, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim, Philips Kokoh Prasetyo
Research Collection School Of Computing and Information Systems
Analyzing job hopping behavior is important for theunderstanding of job preference and career progression of working individuals.When analyzed at the workforce population level, job hop analysis helps to gaininsights of talent flow and organization competition. Traditionally, surveysare conducted on job seekers and employers to study job behavior. While surveysare good at getting direct user input to specially designed questions, they areoften not scalable and timely enough to cope with fast-changing job landscape.In this paper, we present a data science approach to analyze job hops performedby about 490,000 working professionals located in a city using their publiclyshared profiles. We develop several …
Guest Editor's Introduction To The Special Issue On Source Code Analysis And Manipulation (Scam 2015),
2017
Singapore Management University
Guest Editor's Introduction To The Special Issue On Source Code Analysis And Manipulation (Scam 2015), Foutse Khomh, David Lo, Michael W. Godfrey
Research Collection School Of Computing and Information Systems
We are happy to introduce you to this special issue that presents selected papers from the 15th IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2015). SCAM is a leading conference that brings together researchers and practitioners working on theory, techniques, and applications that concern analysis and/or manipulation of the source code of computer systems. While much attention in the wider software engineering community is properly directed towards other aspects of systems development and evolution, such as specification, design, and requirements engineering, it is the source code that contains the only precise description of the behavior of …
Highly Efficient Mining Of Overlapping Clusters In Signed Weighted Networks,
2017
Leibniz University of Hanover
Highly Efficient Mining Of Overlapping Clusters In Signed Weighted Networks, Tuan-Anh Hoang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
In many practical contexts, networks are weighted as their links are assigned numerical weights representing relationship strengths or intensities of inter-node interaction. Moreover, the links' weight can be positive or negative, depending on the relationship or interaction between the connected nodes. The existing methods for network clustering however are not ideal for handling very large signed weighted networks. In this paper, we present a novel method called LPOCSIN (short for "Linear Programming based Overlapping Clustering on Signed Weighted Networks") for efficient mining of overlapping clusters in signed weighted networks. Different from existing methods that rely on computationally expensive cluster cohesiveness …
Unsupervised Topic Hypergraph Hashing For Efficient Mobile Image Retrieval,
2017
Singapore Management University
Unsupervised Topic Hypergraph Hashing For Efficient Mobile Image Retrieval, Lei Zhu, Jialie Shen, Liang Xie, Zhiyong Cheng
Research Collection School Of Computing and Information Systems
Hashing compresses high-dimensional features into compact binary codes. It is one of the promising techniques to support efficient mobile image retrieval, due to its low data transmission cost and fast retrieval response. However, most of existing hashing strategies simply rely on low-level features. Thus, they may generate hashing codes with limited discriminative capability. Moreover, many of them fail to exploit complex and high-order semantic correlations that inherently exist among images. Motivated by these observations, we propose a novel unsupervised hashing scheme, called topic hypergraph hashing (THH), to address the limitations. THH effectively mitigates the semantic shortage of hashing codes by …
Interactive Social Recommendation,
2017
Tsinghua University
Interactive Social Recommendation, Xin Wang, Steven C. H. Hoi, Chenghao Liu, Martin Ester
Research Collection School Of Computing and Information Systems
Social recommendation has been an active research topic over the last decade, based on the assumption that social information from friendship networks is beneficial for improving recommendation accuracy, especially when dealing with cold-start users who lack sufficient past behavior information for accurate recommendation. However, it is nontrivial to use such information, since some of a person's friends may share similar preferences in certain aspects, but others may be totally irrelevant for recommendations. Thus one challenge is to explore and exploit the extend to which a user trusts his/her friends when utilizing social information to improve recommendations. On the other hand, …
Second-Order Online Active Learning And Its Applications,
2017
Institute of High Performance Computing
Second-Order Online Active Learning And Its Applications, Shuji Hao, Jing Lu, Peilin Zhao, Chi Zhang, Steven C. H. Hoi, Chunyan Miao
Research Collection School Of Computing and Information Systems
The goal of online active learning is to learn predictive models from a sequence of unlabeled data given limited label querybudget. Unlike conventional online learning tasks, online active learning is considerably more challenging because of two reasons.Firstly, it is difficult to design an effective query strategy to decide when is appropriate to query the label of an incoming instance givenlimited query budget. Secondly, it is also challenging to decide how to update the predictive models effectively whenever the true labelof an instance is queried. Most existing approaches for online active learning are often based on a family of first-order online …
Predicting Indoor Crowd Density Using Column-Structured Deep Neural Network,
2017
Shizuoko University
Predicting Indoor Crowd Density Using Column-Structured Deep Neural Network, Akihito Sudo, Teck Hou (Deng Dehao) Teng, Hoong Chuin Lau, Yoshihide Sekimoto
Research Collection School Of Computing and Information Systems
This work proposes a deep neural network approach known as the column-structured deep neural network (COL-DNN-R) for predicting crowd density in an indoor environment using historical Wi-Fi traces of individual visitors. With a structure designed to minimize feature engineering, COL-DNN accepts raw features such as crowd density, opening and closing hours and peak visitor counts for extracting features. The extracted features are used by a regression model R for predicting the crowd densities. Standard regression models such as MLP, RF and SVM can be used as R. Experiments are performed to investigate the effect of feature representation and model structure …
A Fast Trajectory Outlier Detection Approach Via Driving Behavior Modeling,
2017
Fudan University
A Fast Trajectory Outlier Detection Approach Via Driving Behavior Modeling, Hao Wu, Weiwei Sun, Baihua Zheng
Research Collection School Of Computing and Information Systems
Trajectory outlier detection is a fundamental building block for many location-based service (LBS) applications, with a large application base. We dedicate this paper on detecting the outliers from vehicle trajectories efficiently and effectively. In addition, we want our solution to be able to issue an alarm early when an outlier trajectory is only partially observed (i.e., the trajectory has not yet reached the destination). Most existing works study the problem on general Euclidean trajectories and require accesses to the historical trajectory database or computations on the distance metric that are very expensive. Furthermore, few of existing works consider some specific …
Selective Value Coupling Learning For Detecting Outliers In High-Dimensional Categorical Data,
2017
Singapore Management University
Selective Value Coupling Learning For Detecting Outliers In High-Dimensional Categorical Data, Guansong Pang, Hongzuo Xu, Cao Longbing, Wentao Zhao
Research Collection School Of Computing and Information Systems
This paper introduces a novel framework, namely SelectVC and its instance POP, for learning selective value couplings (i.e., interactions between the full value set and a set of outlying values) to identify outliers in high-dimensional categorical data. Existing outlier detection methods work on a full data space or feature subspaces that are identified independently from subsequent outlier scoring. As a result, they are significantly challenged by overwhelming irrelevant features in high-dimensional data due to the noise brought by the irrelevant features and its huge search space. In contrast, SelectVC works on a clean and condensed data space spanned by selective …
Vireo @ Trecvid 2017: Video-To-Text, Ad-Hoc Video Search And Video Hyperlinking,
2017
Singapore Management University
Vireo @ Trecvid 2017: Video-To-Text, Ad-Hoc Video Search And Video Hyperlinking, Phuong Anh Nguyen, Qing Li, Zhi-Qi Cheng, Yi-Jie Lu, Hao Zhang, Xiao Wu, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
In this paper, we describe the systems developed for Video-to-Text (VTT), Ad-hoc Video Search (AVS) and Video Hyper-linking (LNK) tasks at TRECVID 2017 [1] and the achieved results.
