Incremental Kernel Null Space Discriminant Analysis for Novelty Detection

2017 ◽  
Author(s):  
Juncheng Liu ◽  
Zhouhui Lian ◽  
Yi Wang ◽  
Jianguo Xiao
Keyword(s):  
Discriminant Analysis ◽  
Null Space ◽  
Novelty Detection

scholarly journals A Complete Subspace Analysis of Linear Discriminant Analysis and Its Robust Implementation

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Zhicheng Lu ◽  
Zhizheng Liang
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Null Space ◽  
Small Sample Size ◽  
Classification Performance ◽  
Optimization Criterion ◽  
Small Sample ◽  
Scatter Matrix ◽  
Linear Discriminant ◽  
Maximum Margin Criterion

Linear discriminant analysis has been widely studied in data mining and pattern recognition. However, when performing the eigen-decomposition on the matrix pair (within-class scatter matrix and between-class scatter matrix) in some cases, one can find that there exist some degenerated eigenvalues, thereby resulting in indistinguishability of information from the eigen-subspace corresponding to some degenerated eigenvalue. In order to address this problem, we revisit linear discriminant analysis in this paper and propose a stable and effective algorithm for linear discriminant analysis in terms of an optimization criterion. By discussing the properties of the optimization criterion, we find that the eigenvectors in some eigen-subspaces may be indistinguishable if the degenerated eigenvalue occurs. Inspired from the idea of the maximum margin criterion (MMC), we embed MMC into the eigen-subspace corresponding to the degenerated eigenvalue to exploit discriminability of the eigenvectors in the eigen-subspace. Since the proposed algorithm can deal with the degenerated case of eigenvalues, it not only handles the small-sample-size problem but also enables us to select projection vectors from the null space of the between-class scatter matrix. Extensive experiments on several face images and microarray data sets are conducted to evaluate the proposed algorithm in terms of the classification performance, and experimental results show that our method has smaller standard deviations than other methods in most cases.

A new and fast implementation for null space based linear discriminant analysis

2010 ◽  
Vol 43 (4) ◽  
pp. 1373-1379 ◽  
Author(s):  
Delin Chu ◽  
Goh Siong Thye
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Null Space ◽  
Linear Discriminant ◽  
Fast Implementation

scholarly journals Transformation Technique for Null Space-Based Linear Discriminant Analysis with Lagrange Method

2013 ◽  
Vol 38C (2) ◽  
pp. 208-212 ◽  
Author(s):  
Yuxi Hou ◽  
Hwang-Ki Min ◽  
Iickho Song ◽  
Myeong Soo Choi ◽  
Sun Park ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Null Space ◽  
Transformation Technique ◽  
Lagrange Method ◽  
Linear Discriminant

FUSING GLOBAL AND LOCAL COMPLETE LINEAR DISCRIMINANT FEATURES BY FUZZY INTEGRAL FOR FACE RECOGNITION

2008 ◽  
Vol 22 (07) ◽  
pp. 1427-1445 ◽  
Author(s):  
CHENGYUAN ZHANG ◽  
QIUQI RUAN ◽  
YI JIN
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Null Space ◽  
Information Aggregation ◽  
Recognition Algorithm ◽  
Fuzzy Integral ◽  
Fisher Discriminant Analysis ◽  
Linear Discriminant ◽  
Global And Local

Face recognition becomes very difficult in a complex environment, and the combination of multiple classifiers is a good solution to this problem. A novel face recognition algorithm GLCFDA-FI is proposed in this paper, which fuses the complementary information extracted by complete linear discriminant analysis from the global and local features of a face to improve the performance. The Choquet fuzzy integral is used as the fusing tool due to its suitable properties for information aggregation. Experiments are carried out on the CAS-PEAL-R1 database, the Harvard database and the FERET database to demonstrate the effectiveness of the proposed method. Results also indicate that the proposed method GLCFDA-FI outperforms five other commonly used algorithms — namely, Fisherfaces, null space-based linear discriminant analysis (NLDA), cascaded-LDA, kernel-Fisher discriminant analysis (KFDA), and null-space based KFDA (NKFDA).

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