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Locality preserving projection (LPP) is a promising manifold learning approach for dimensionality reduction. However, it often encounters small sample size (3S) problem in face recognition tasks. To overcome this limitation, this paper proposes a discrete sine transform (DST) feature extraction approach and develops a DST-feature based LPP algorithm for face recognition. The proposed method has been...
The face recognition task involves extraction of unique features from the human face. Manifold learning methods are proposed to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. LPP should be seen as an alternative to Principal Component Analysis (PCA). When the high dimensional data lies on a low dimensional manifold embedded in the ambient...
The high number of features in many machine vision applications has a major impact on the performance of machine learning algorithms. Feature selection (FS) is an avenue to dimensionality reduction. Evolutionary search techniques have been very promising in finding solutions in the exponentially growing search space of FS problems. This paper proposes a genetic programming (GP) approach to FS where...
We propose in this paper an improved manifold learning method called two-directional two-dimensional discriminant locality preserving projections, (2D)2-DLPP, for efficient image recognition. As the existing method of two-dimensional discriminant locality preserving projections (2D-DLPP) mainly relies upon the local structure information in the rows of images, we first derive an alternative 2D-DLPP...
In this paper, we derive a data mining framework to analyze 3D features on human faces. The framework leverages kernel density estimators, genetic algorithm and an information complexity criterion to identify discriminant feature-clusters of lower dimensionality. We apply this framework on human face anthropometry data of 32 features collected from each of the 300 3D face mesh models. The feature-subsets...
Recently, locality sensitive discriminant analysis (LSDA) was proposed for dimensionality reduction. As far as matrix data, such as images, they are often vectorized for LSDA algorithm to find the intrinsic manifold structure. Such a matrix-to-vector transform may cause the loss of some structural information residing in original 2D images. Firstly, this paper proposes an algorithm named two-dimensional...
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