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Online Similarity Learning for Big Data with Overfitting
Cong Y(丛杨); Liu J(刘霁); Fan BJ(范保杰); Zeng P(曾鹏); Yu HB(于海斌); Luo JB(罗杰波)
Source PublicationIEEE Transactions on Big Data
Indexed ByEI
EI Accession number20181104888919
Contribution Rank1
Funding OrganizationNSFC (61375014, U1613214, 61533015), CAS Youth Innovation Promotion Association Scholarship (2012163) and also the foundation of Chinese Scholarship Council.
KeywordOnline Learning Similarity Learning Low Rank Sparse Representation Feature Selection Overfitting Redundancy

In this paper, we propose a general model to address the overfitting problem in online similarity learning for big data, which is generally generated by two kinds of redundancies: 1) feature redundancy, that is there exists redundant (irrelevant) features in the training data; 2) rank redundancy, that is non-redundant (or relevant) features lie in a low rank space. To overcome these, our model is designed to obtain a simple and robust metric matrix through detecting the redundant rows and columns in the metric matrix and constraining the remaining matrix to a low rank space. To reduce feature redundancy, we employ the group sparsity regularization, i.e., the `2;1 norm, to encourage a sparse feature set. To address rank redundancy, we adopt the low rank regularization, the max norm, instead of calculating the SVD as in traditional models using the nuclear norm. Therefore, our model can not only generate a low rank metric matrix to avoid overfitting, but also achieves feature selection simultaneously. For model optimization, an online algorithm based on the stochastic proximal method is derived to solve this problem efficiently with the complexity of O(d2). To validate the effectiveness and efficiency of our algorithms, we apply our model to online scene categorization and synthesized data and conduct experiments on various benchmark datasets with comparisons to several state-of-the-art methods. Our model is as efficient as the fastest online similarity learning model OASIS, while performing generally as well as the accurate model OMLLR. Moreover, our model can exclude irrelevant / redundant feature dimension simultaneously.

Document Type期刊论文
Corresponding AuthorCong Y(丛杨)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Department of Computer Science, University of Rochester, Rochester, NY 14611 USA
3.College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, 210042 China
Recommended Citation
GB/T 7714
Cong Y,Liu J,Fan BJ,et al. Online Similarity Learning for Big Data with Overfitting[J]. IEEE Transactions on Big Data,2018,4(1):78-89.
APA Cong Y,Liu J,Fan BJ,Zeng P,Yu HB,&Luo JB.(2018).Online Similarity Learning for Big Data with Overfitting.IEEE Transactions on Big Data,4(1),78-89.
MLA Cong Y,et al."Online Similarity Learning for Big Data with Overfitting".IEEE Transactions on Big Data 4.1(2018):78-89.
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