<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>Dimension reduction is an important topic in data mining and machine learning. Especially dimension reduction combined with feature fusion is an effective preprocessing step when the data are described by multiple feature sets. Canonical Correlation Analysis (CCA) and Discriminative Canonical Correlation Analysis (DCCA) are feature fusion methods based on correlation. However, they are different in that DCCA is a supervised method utilizing class label information, while CCA is an unsupervised method. It has been shown that the classification performance of DCCA is superior to that of CCA due to the discriminative power using class label information. On the other hand, Linear Discriminant Analysis (LDA) is a supervised dimension reduction method and it is known as a special case of CCA. In this paper, we analyze the relationship between DCCA and LDA, showing that the projective directions by DCCA are equal to the ones obtained from LDA with respect to an orthogonal transformation. Using the relation with LDA, we propose a new method that can enhance the performance of DCCA. The experimental results show that the proposed method exhibits better classification performance than the original DCCA.</p></abstract>

Dimension reduction is an important topic in data mining and machine learning. Especially dimension reduction combined with feature fusion is an effective preprocessing step when the data are described by multiple feature sets. Canonical Correlation Analysis (CCA) and Discriminative Canonical Correlation Analysis (DCCA) are feature fusion methods based on correlation. However, they are different in that DCCA is a supervised method utilizing class label information, while CCA is an unsupervised method. It has been shown that the classification performance of DCCA is superior to that of CCA due to the discriminative power using class label information. On the other hand, Linear Discriminant Analysis (LDA) is a supervised dimension reduction method and it is known as a special case of CCA. In this paper, we analyze the relationship between DCCA and LDA, showing that the projective directions by DCCA are equal to the ones obtained from LDA with respect to an orthogonal transformation. Using the relation with LDA, we propose a new method that can enhance the performance of DCCA. The experimental results show that the proposed method exhibits better classification performance than the original DCCA.

Analysis of correlation based dimension reduction methods

Department of Computer Science and Engineering, Chungnam National University, 220 Gung-dong, Yuseong-gu, Daejeon, 305-764, Korea

International Journal of Applied Mathematics and Computer Science

{"article-title":"Analysis of correlation based dimension reduction methods"}

Dimension reduction is an important topic in data mining and machine learning. Especially dimension reduction combined with feature fusion is an effective...

Analysis of correlation based dimension reduction methods

Publicado en línea: 22 sept 2011

Páginas: 549 - 558

DOI: https://doi.org/10.2478/v10006-011-0043-9

Palabras clave
canonical correlation analysis, dimension reduction, discriminative canonical correlation analysis, feature fusion, linear discriminant analysis

This content is open access.

Analysis of correlation based dimension reduction methods

Publicado en línea: 22 sept 2011

Páginas: 549 - 558

DOI: https://doi.org/10.2478/v10006-011-0043-9

Palabras clavecanonical correlation analysis, dimension reduction, discriminative canonical correlation analysis, feature fusion, linear discriminant analysis

This content is open access.

Palabras clave
canonical correlation analysis, dimension reduction, discriminative canonical correlation analysis, feature fusion, linear discriminant analysis