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Multi-Label Output Codes using Canonical Correlation Analysis
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BibTeX
@MISC{Zhang_multi-labeloutput,
author = {Yi Zhang and Jeff Schneider},
title = {Multi-Label Output Codes using Canonical Correlation Analysis},
year = {}
}
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Abstract
Traditional error-correctingoutput codes (E-COCs) decompose a multi-class classification problem into many binary problems. Although it seems natural to use ECOCs for multi-label problems as well, doing so naively createsissues related to: the validity of the encoding, the efficiency of the decoding, the predictabilityofthegeneratedcodeword,and the exploitation of the label dependency. Using canonical correlation analysis, we propose an error-correcting code for multi-label classification. Labeldependencyischaracterized as the most predictable directions in the label space, which are extracted as canonical output variates and encoded into the codeword. Predictions for the codeword define a graphical model of labels with both Bernoulli potentials (from classifiers on the labels) and Gaussian potentials (from regression on the canonical output variates). Decoding is performed by mean-field approximation. We establish connections between the proposed code and research areas such as compressed sensing and ensemble learning. Some of these connections contribute to better understanding of the new code, and others lead to practical improvements in code design. In our empirical study, the proposed code leads to substantial improvements compared to various competitors in music emotion classification and outdoor scene recognition. 1
Keyphrases
canonical correlation analysis multi-label output code canonical output variate predictable direction label space outdoor scene recognition bernoulli potential mean-field approximation error-correcting code multi-label problem substantial improvement gaussian potential compressed sensing practical improvement empirical study ensemble learning many binary problem new code music emotion classification multi-label classification code design multi-class classification problem graphical model label dependency various competitor research area traditional error-correctingoutput code
