NDLI: Sparse Online Relative Similarity Learning

Content Provider	IEEE Xplore Digital Library
Author	Dezhong Yao Peilin Zhao Chen Yu Hai Jin Bin Li
Copyright Year	2015
Description	Author affiliation: Sch. of Comput. Sci. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan, China (Dezhong Yao; Chen Yu; Hai Jin) \|\| Data Analytics Dept., A*STAR, Singapore, Singapore (Peilin Zhao) \|\| Econ. & Manage. Sch., Wuhan Univ., Wuhan, China (Bin Li)
Abstract	For many data mining and machine learning tasks, the quality of a similarity measure is the key for their performance. To automatically find a good similarity measure from datasets, metric learning and similarity learning are proposed and studied extensively. Metric learning will learn a Mahalanobis distance based on positive semi-definite (PSD) matrix, to measure the distances between objectives, while similarity learning aims to directly learn a similarity function without PSD constraint so that it is more attractive. Most of the existing similarity learning algorithms are online similarity learning method, since online learning is more scalable than offline learning. However, most existing online similarity learning algorithms learn a full matrix with $d^{2}$ parameters, where d is the dimension of the instances. This is clearly inefficient for high dimensional tasks due to its high memory and computational complexity. To solve this issue, we introduce several Sparse Online Relative Similarity (SORS) learning algorithms, which learn a sparse model during the learning process, so that the memory and computational cost can be significantly reduced. We theoretically analyze the proposed algorithms, and evaluate them on some real-world high dimensional datasets. Encouraging empirical results demonstrate the advantages of our approach in terms of efficiency and efficacy.
Starting Page	529
Ending Page	538
File Size	321636
Page Count	10
File Format	PDF
ISSN	15504786
e-ISBN	9781467395045
DOI	10.1109/ICDM.2015.100
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Sparse matrices Image retrieval Machine learning algorithms Algorithm design and analysis Data mining Correlation Data stream mining High-dimensional similarity learning Sparse Online learning
Content Type	Text
Resource Type	Article

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