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Full-Text Articles in Databases and Information Systems
Semi-Supervised Distance Metric Learning For Collaborative Image Retrieval And Clustering, Steven C. H. Hoi, Wei Liu, Shih-Fu Chang
Semi-Supervised Distance Metric Learning For Collaborative Image Retrieval And Clustering, Steven C. H. Hoi, Wei Liu, Shih-Fu Chang
Research Collection School Of Computing and Information Systems
Learning a good distance metric plays a vital role in many multimedia retrieval and data mining tasks. For example, a typical content-based image retrieval (CBIR) system often relies on an effective distance metric to measure similarity between any two images. Conventional CBIR systems simply adopting Euclidean distance metric often fail to return satisfactory results mainly due to the well-known semantic gap challenge. In this article, we present a novel framework of Semi-Supervised Distance Metric Learning for learning effective distance metrics by exploring the historical relevance feedback log data of a CBIR system and utilizing unlabeled data when log data are …
K-Anonymity In The Presence Of External Databases, Dimitris Sacharidis, Kyriakos Mouratidis, Dimitris Papadias
K-Anonymity In The Presence Of External Databases, Dimitris Sacharidis, Kyriakos Mouratidis, Dimitris Papadias
Research Collection School Of Computing and Information Systems
The concept of k-anonymity has received considerable attention due to the need of several organizations to release microdata without revealing the identity of individuals. Although all previous k-anonymity techniques assume the existence of a public database (PD) that can be used to breach privacy, none utilizes PD during the anonymization process. Specifically, existing generalization algorithms create anonymous tables using only the microdata table (MT) to be published, independently of the external knowledge available. This omission leads to high information loss. Motivated by this observation we first introduce the concept of k-join-anonymity (KJA), which permits more effective generalization to reduce the …
Information-Quality Aware Routing In Event-Driven Sensor Networks, Hwee Xian Tan, Mun-Choon Chan, Wendong Xiao, Peng-Yong Kong, Chen-Khong Tham
Information-Quality Aware Routing In Event-Driven Sensor Networks, Hwee Xian Tan, Mun-Choon Chan, Wendong Xiao, Peng-Yong Kong, Chen-Khong Tham
Research Collection School Of Computing and Information Systems
Upon the occurrence of a phenomenon of interest in a wireless sensor network, multiple sensors may be activated, leading to data implosion and redundancy. Data aggregation and/or fusion techniques exploit spatio-temporal correlation among sensory data to reduce traffic load and mitigate congestion. However, this is often at the expense of loss in Information Quality (IQ) of data that is collected at the fusion center. In this work, we address the problem of finding the least-cost routing tree that satisfies a given IQ constraint. We note that the optimal least-cost routing solution is a variation of the classical NP-hard Steiner tree …