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2011 | 21 | 3 | 549-558
Tytuł artykułu

Analysis of correlation based dimension reduction methods

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Tom
21
Numer
3
Strony
549-558
Opis fizyczny
Daty
wydano
2011
otrzymano
2010-05-12
poprawiono
2010-10-22
poprawiono
2011-01-05
Twórcy
  • Department of Computer Science and Engineering, Chungnam National University, 220 Gung-dong, Yuseong-gu, Daejeon, 305-764, Korea
  • Department of Computer Science and Engineering, Chungnam National University, 220 Gung-dong, Yuseong-gu, Daejeon, 305-764, Korea
Bibliografia
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv21i3p549bwm
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