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In this paper, a novel framework for facial expression recognition is proposed, which improves the conventional feature extraction technique to further exploit distinctive characters for each label. To reduce the effect from unrelated features for facial expression recognition, a denoising mechanism is introduced. After denoising, to keep the connection between expression labels and whiten features...
In the past few years, manifold learning and sparse representation have been widely used for feature extraction and dimensionality reduction. The sparse representation technique shows that one sample can be linearly recovered by the others in a data set. Based on this, sparsity preserving projections (SPP) has recently been proposed, which simply minimizes the sparse reconstructive errors among training...
Most manifold learning based methods preserve the original neighbor relationships to pursue the discriminating power. Thus, structure information of data distribution might be neglected and destroyed in low-dimensional space in a sense. In this paper, a novel supervised method, called Locality Preserving Embedding (LPE), is proposed to feature extraction and dimensionality reduction. LPE gives a low-dimensional...
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...
A new unsupervised discriminant projection for dimensionality reduction of high dimensional data is presented in this paper. The new method is a linear projection based on both the local and nonlocal statistically quantities. The discriminant criterion function be characterized by difference between the nonlocal scatter and the local scatter of feature vector, seeking to find a group of projection...
This paper presents a new method of dimensionality reduction of high dimensional data. The new discriminant criterion function be characterized by between the nonlocal scatter and the local scatter, and to directly construct between local scatter matrix and nonlocal scatter matrix by sample image matrixes. The criterion main purpose is to find a group of projection axis that simultaneously maximizes...
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