Discriminant analysis in correlation similarity measure space

Correlation is one of the most widely used similarity measures in machine learning like Euclidean and Mahalanobis distances. However, compared with proposed numerous discriminant learning algorithms in distance metric space, only a very little work has been conducted on this topic using correlation similarity measure. In this paper, we propose a novel discriminant learning algorithm in correlation measure space, Correlation Discriminant Analysis (CDA). In this framework, based on the definitions of within-class correlation and between-class correlation, the optimum transformation can be sought for to maximize the difference between them, which is in accordance with good classification performance empirically. Under different cases of the transformation, different implementations of the algorithm are given. Extensive empirical evaluations of CDA demonstrate its advantage over alternative methods.

• Type: Conference
• Year: 2007
• Where: ICML
• Authors: Yong Ma, Shihong Lao, Erina Takikawa, Masato Kawade
• Keywords: Correlation Discriminant Analysis, Correlation Measure Space, Correlation Similarity Measure, ICML 2007, Machine Learning