Local Linear Discriminant Analysis Framework Using Sample Neighbors

Abstract

The linear discriminant analysis (LDA) is a very popular linear feature extraction approach. The algorithms of LDA usually perform well under the following two assumptions. The first assumption is that the global data structure is consistent with the local data structure. The second assumption is that the input data classes are Gaussian distributions. However, in real-world applications, these assumptions are not always satisfied. In this paper, we propose an improved LDA framework, the local LDA (LLDA), which can perform well without needing to satisfy the above two assumptions. Our LLDA framework can effectively capture the local structure of samples. According to different types of local data structure, our LLDA framework incorporates several different forms of linear feature extraction approaches, such as the classical LDA and principal component analysis. The proposed framework includes two LLDA algorithms: a vector-based LLDA algorithm and a matrix-based LLDA (MLLDA) algorithm. MLLDA is directly applicable to image recognition, such as face recognition. Our algorithms need to train only a small portion of the whole training set before testing a sample. They are suitable for learning large-scale databases especially when the input data dimensions are very high and can achieve high classification accuracy. Extensive experiments show that the proposed algorithms can obtain good classification results.

Extended semi-supervised fuzzy learning method for nonlinear outliers via pattern discovery

“…The conventional LDA methods [43][44][45][46][47] aim at maximizing the ratio of between-class scatter matrix to within-class scatter matrix. Given a set of n i samples belonging to class c i , we can define the mean of each class as:…”

Mixture Subclass Discriminant Analysis Link to Restricted Gaussian Model and Other Generalizations

“…This problem has been mostly addressed by using kernel extensions of LDA, [9], [10] or methods that use local linear discriminant analyzers to learn the nonlinear data structure [2], [11]. However, the SSS problem remains, and to address it similar solutions to those discussed above are exploited for both the kernel-based [12], [13] and local-based [14] LDA variants.…”

Robust Face Recognition Via Gabor Feature and Sparse Representation

“…Chen [17] present a local discriminant embedding (LDE) method for popular learning and pattern classification.Yan [18] propose a graph embedding framework to develop a new dimension reduction algorithm.Cai [19] propose a subspace method of spatial smoothing for face recognition and orthogonal Laplacianfaces face recognition [21].Fan [20] raise an improved LDA framework (LLDA) that can effectively capture the local structure of the samples.…”