Classification of streaming big data with misses

Fatemeh Sheikholesalmi, Morteza Mardani, Georgios B. Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


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 languageEnglish (US)
Title of host publicationConference Record of the 48th Asilomar Conference on Signals, Systems and Computers
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781479982974
StatePublished - Apr 24 2015
Event48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: Nov 2 2014Nov 5 2014

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Other48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Country/TerritoryUnited States
CityPacific Grove

Bibliographical note

Publisher Copyright:
© 2014 IEEE.


  • D.2. Machine Learning and Statistical Signal Processing
  • E.7. High-Dimensional Large-Scale Data
  • Technical Area


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