Abstract
Canonical correlation analysis (CCA) is an extremely useful technique in many applications that involve simultaneous analysis of a large number of variables of distinct types. In this paper, we present new methods of performing correlation analysis using gradient descent where canonical variates and correlations are computed serially. The CCA is formulated as a solution of constrained and nonconstrained optimization problems. Simulations are also provided to demonstrate the performance of the proposed techniques.
| Original language | English (US) |
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| Pages (from-to) | III309-III312 |
| Journal | Proceedings - IEEE International Symposium on Circuits and Systems |
| Volume | 3 |
| State | Published - 2004 |
| Event | 2004 IEEE International Symposium on Circuits and Systems - Proceedings - Vancouver, BC, Canada Duration: May 23 2004 → May 26 2004 |