Semi-Supervised Sparse Metric Learning Using Alternating Linearization Optimization

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dc.contributor.author Liu, W
dc.contributor.author Ma, S
dc.contributor.author Tao, D
dc.contributor.author Liu, J
dc.contributor.author Liu, P
dc.contributor.editor Rao, RB
dc.contributor.editor Tomkins, A
dc.contributor.editor Yang, Q
dc.contributor.editor Krishnapuram, B
dc.date.accessioned 2012-10-12T03:36:17Z
dc.date.issued 2010-01
dc.identifier.citation Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data, 2010, pp. 1139 - 1147
dc.identifier.isbn 978-1-4503-0055-1
dc.identifier.other E1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/19142
dc.description.abstract In plenty of scenarios, data can be represented as vectors and then mathematically abstracted as points in a Euclidean space. Because a great number of machine learning and data mining applications need proximity measures over data, a simple and universal distance metric is desirable, and metric learning methods have been explored to produce sensible distance measures consistent with data relationship. However, most existing methods suffer from limited labeled data and expensive training. In this paper, we address these two issues through employing abundant unlabeled data and pursuing sparsity of metrics, resulting in a novel metric learning approach called semi-supervised sparse metric learning. Two important contributions of our approach are: 1) it propagates scarce prior affinities between data to the global scope and incorporates the full affinities into the metric learning; and 2) it uses an efficient alternating linearization method to directly optimize the sparse metric. Compared with conventional methods, ours can effectively take advantage of semi-supervision and automatically discover the sparse metric structure underlying input data patterns. We demonstrate the efficacy of the proposed approach with extensive experiments carried out on six datasets, obtaining clear performance gains over the state-of-the-arts.
dc.publisher Association for Computing Machinery, Inc. (ACM)
dc.relation.isbasedon 10.1145/1835804.1835947
dc.subject Metric learning, semi-supervised sparse metric learning, sparse inverse covariance estimation, alternating linearization
dc.subject Metric learning, semi-supervised sparse metric learning, sparse inverse covariance estimation, alternating linearization
dc.title Semi-Supervised Sparse Metric Learning Using Alternating Linearization Optimization
dc.type Conference Proceeding
dc.parent Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data
dc.journal.number en_US
dc.publocation Danvers, MA, USA en_US
dc.publocation Danvers, MA, USA
dc.identifier.startpage 1139 en_US
dc.identifier.endpage 1147 en_US
dc.cauo.name FEIT.A/DRsch Ctr Quantum Computat'n & Intelligent Systs en_US
dc.conference Verified OK en_US
dc.conference ACM SIGKDD International Conference on Knowledge Discovery and Data
dc.for 080109 Pattern Recognition and Data Mining
dc.personcode 0000073899 en_US
dc.personcode 0000073900 en_US
dc.personcode 111502 en_US
dc.personcode 0000073901 en_US
dc.personcode 0000073902 en_US
dc.percentage 100 en_US
dc.classification.name Pattern Recognition and Data Mining en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom ACM SIGKDD International Conference on Knowledge Discovery and Data en_US
dc.date.activity 20100725 en_US
dc.date.activity 2010-07-25
dc.location.activity Washington, DC, USA en_US
dc.location.activity Washington, DC, USA
dc.description.keywords Metric learning, semi-supervised sparse metric learning, sparse inverse covariance estimation, alternating linearization en_US
dc.description.keywords direct-to-consumer, prescription medicines, pharmacy consumer, internet
dc.description.keywords Metric learning, semi-supervised sparse metric learning, sparse inverse covariance estimation, alternating linearization
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems


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