DMCA
Sparse canonical correlation analysis (2011)
| Venue: | Machine Learning Journal |
| Citations: | 22 - 0 self |
BibTeX
@INPROCEEDINGS{Hardoon11sparsecanonical,
author = {David R. Hardoon and John Shawe-taylor},
title = {Sparse canonical correlation analysis},
booktitle = {Machine Learning Journal},
year = {2011}
}
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Abstract
We present a novel method for solving Canonical Correlation Analysis (CCA) in a sparse convex framework using a least squares approach. The presented method focuses on the scenario when one is interested in (or limited to) a primal representation for the first view while having a dual representation for the second view. Sparse CCA (SCCA) minimises the number of features used in both the primal and dual projections while maximising the correlation between the two views. The method is demonstrated on two paired corpuses of English-French and English-Spanish for mate-retrieval. We are able to observe, in the mate-retreival, that when the number of the original features is large SCCA outperforms Kernel CCA (KCCA), learning the common semantic space from a sparse set of features. 1
Keyphrases
sparse canonical correlation analysis primal representation second view sparse cca original feature large scca outperforms kernel cca first view dual projection dual representation presented method sparse convex framework paired corpus common semantic space canonical correlation analysis square approach sparse set novel method
