Nonlinear Feature Extraction using Class-augmented Kernel PCA
- Nonlinear Feature Extraction using Class-augmented Kernel PCA
- 박명수; 오상록
- 클래스 정보; 비선형 특징추출; 분류를 위한 특징; Nonlinear Feature Extraction; Disciminant Features
- Issue Date
- VOL 48, NO 5, 7-12
- In this papwer, we propose a new feature extraction method, named as Class-augmented Kernel Principal Component
Analysis (CA-KPCA), which can extract nonlinear features for classification. Among the subspace method that was being
widely used for feature extraction, Class-augmented Principal Component Analysis (CA-PCA) is a recently one that can
extract features for a accurate classification without computational difficulties of other methods such as Linear Discriminant
Analysis (LDA). However, the features extracted by CA-PCA is still restricted to be in a linear subspace of the original
data space, which limites the use of this method for various problems requiring nonlinear features. To resolve this
limitation, we apply a kernel trick to develop a new version of CA-PCA to extract nonlinear features, and evaluate its
performance by experiments using data sets in the UCI Machine Learning Repository.
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