Recycled linear classifiers for multiclass classification

Akshay Soni, Jarvis Haupt, Fatih Porikli

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


Many machine learning applications employ a multiclass classification stage that uses multiple binary linear classifiers as building blocks. Among these, commonly used strategies such as one-vs-one classification can require learning a large number of hyperplanes, even when the number of classes to be discriminated among is modest. Further, when the data being classified is inherently high-dimensional, the storage and computational complexity associated with the application of multiple linear classifiers can ignite critical resource management issues. This work describes a novel multiclass classification method based on efficient use of a single 'recycled' linear classifier (or ReLiC), which addresses these storage and implementation complexity issues. The proposed approach amounts to constraining the entire collection of hyperplanes to be circularly-shifted versions of each other, enabling classification procedures that may be implemented with efficient operations, such as circular convolution (which can be efficiently computed using transform domain techniques), and simple sampling/thresholding operations. We show that the optimization task associated with our proposed approach can be formulated as a quadratic program, and we introduce an efficient distributed procedure for its solution based on an alternating direction method of multipliers. Simulation results demonstrate that the performance of the proposed approach is comparable with the more complex, traditional multiclass linear classification strategies, suggesting the proposed approach is a viable alternative in large-scale data classification tasks.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Print)9781479928927
StatePublished - 2014
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: May 4 2014May 9 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149


Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014


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