Maximum Variance Hashing via Column Generation
Joint Authors
Luo, Lei
Zhang, Chao
Qin, Yongrui
Zhang, Chunyuan
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-05-15
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
With the explosive growth of the data volume in modern applications such as web search and multimedia retrieval, hashing is becoming increasingly important for efficient nearest neighbor (similar item) search.
Recently, a number of data-dependent methods have been developed, reflecting the great potential of learning for hashing.
Inspired by the classic nonlinear dimensionality reduction algorithm—maximum variance unfolding, we propose a novel unsupervised hashing method, named maximum variance hashing, in this work.
The idea is to maximize the total variance of the hash codes while preserving the local structure of the training data.
To solve the derived optimization problem, we propose a column generation algorithm, which directly learns the binary-valued hash functions.
We then extend it using anchor graphs to reduce the computational cost.
Experiments on large-scale image datasets demonstrate that the proposed method outperforms state-of-the-art hashing methods in many cases.
American Psychological Association (APA)
Luo, Lei& Zhang, Chao& Qin, Yongrui& Zhang, Chunyuan. 2013. Maximum Variance Hashing via Column Generation. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-1009172
Modern Language Association (MLA)
Luo, Lei…[et al.]. Maximum Variance Hashing via Column Generation. Mathematical Problems in Engineering No. 2013 (2013), pp.1-10.
https://search.emarefa.net/detail/BIM-1009172
American Medical Association (AMA)
Luo, Lei& Zhang, Chao& Qin, Yongrui& Zhang, Chunyuan. Maximum Variance Hashing via Column Generation. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-1009172
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1009172