Abstract
Classification is a task of paramount importance for learning tasks involved in nowadays 'Big Data' applications. It is however challenged by the large volume of streaming and possibly incomplete data, and the need for real-time processing. The present paper advocates a novel approach that leverages the intrinsic low-dimensionality of (possibly large-scale) data to design a support-vector-machine (SVM) classifier from feature vectors with misses 'on the fly.' Towards this end, the max-margin cost function is regularized with the nuclear-norm to jointly impute the missing features and design the SVM hyperplane. Iterative batch and online algorithms are developed. Per iteration, a low-dimensional subspace is updated to enable imputation, and the SVM hyperplane is adjusted accordingly. Lightweight firstorder iterations are also devised using stochastic alternating-minimization carried out via simple updates. Preliminary numerical tests corroborate the effectiveness of the novel approach.
Original language | English (US) |
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Title of host publication | Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers |
Editors | Michael B. Matthews |
Publisher | IEEE Computer Society |
Pages | 1051-1055 |
Number of pages | 5 |
ISBN (Electronic) | 9781479982974 |
DOIs | |
State | Published - Apr 24 2015 |
Event | 48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States Duration: Nov 2 2014 → Nov 5 2014 |
Publication series
Name | Conference Record - Asilomar Conference on Signals, Systems and Computers |
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Volume | 2015-April |
ISSN (Print) | 1058-6393 |
Other
Other | 48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 |
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Country/Territory | United States |
City | Pacific Grove |
Period | 11/2/14 → 11/5/14 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.
Keywords
- D.2. Machine Learning and Statistical Signal Processing
- E.7. High-Dimensional Large-Scale Data
- Technical Area