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Principal component analysis (PCA) has been widely applied in the area of computer science. It is well-known that PCA is a popular transform method and the transform result is not directly related to a sole feature component of the original sample. However, in this paper, we try to apply principal components analysis (PCA) to feature selection. The proposed method well addresses the feature selection...
To remove the redundant features extracted by using 2DPCA methods, a face recognition method is presented based on 2DPCA and fuzzy-rough technique in this paper. The proposed method selects the important features for classification by using attribute reduction in fuzzy rough sets theory. The experimental results show the proposed method outperforms the face recognition methods based on 2DPCA.
Feature selection, often used as a pre-processing step to machine learning, is designed to reduce dimensionality, eliminate irrelevant data and improve accuracy. Iris basis is our first attempt to reduce the dimensionality of the problem while focusing only on parts of the scene that effectively identify the individual. Independent component analysis (ICA) is to extract iris feature to recognize iris...
Past work on face detection has emphasized the issues of feature extraction and classification, however, less attention has been given on the critical issue of feature selection. We consider the problem of face and non-face classification from frontal facial images using feature selection and neural networks. We argue that feature selection is an important issue in face and non-face classification...
This paper presents a novel approach of feature selection based on analysis of covariance matrix of training patterns, a correlation-based feature selection method is put forward. An objective measure is proposed and defined. It is shown that for a given set of features, a subset of features that has the highest sum of the correlation coefficients has the tendency to be reduced, if it meets the requirement...
In two-dimensional principal component analysis (2DPCA), 2D face image matrices do not need to be previously transformed into a vector. In this way, the image covariance matrix can be better estimated, compared to the old fashion. The feature is derived from eigenvectors corresponding to the largest eigenvalues of the image covariance matrix for data of all classes. Normally, the number of the largest...
In this letter, a selective kernel principal component analysis (KPCA) algorithm based on high-order statistics is proposed for anomaly detection in hyperspectral imagery. First, KPCA is performed on the original hyperspectral data to fully mine the high-order correlation between spectral bands. Then, the average local singularity (LS) is defined based on the high-order statistics in the local sliding...
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