Comparison of matrix-based linear discriminant analysis methods
نویسندگان
چکیده
منابع مشابه
Comparison of linear discriminant analysis methods for the classification of cancer based on gene expression data
BACKGROUND More studies based on gene expression data have been reported in great detail, however, one major challenge for the methodologists is the choice of classification methods. The main purpose of this research was to compare the performance of linear discriminant analysis (LDA) and its modification methods for the classification of cancer based on gene expression data. METHODS The clas...
متن کاملConvergence proof of matrix dynamics for online linear discriminant analysis
In this paper, we analyze matrix dynamics for online linear discriminant analysis (online LDA). Convergence of the dynamics have been studied for nonsingular cases; our main contribution is an analysis of singular cases, that is a key for efficient calculation without full-size square matrices. All fixed points of the dynamics are identified and their stability is examined. © 2010 Elsevier Inc....
متن کاملComparative Performance of Several Robust Linear Discriminant Analysis Methods
• The problem of the non-robustness of the classical estimates in the setting of the quadratic and linear discriminant analysis has been addressed by many authors: Todorov et al. [19, 20], Chork and Rousseeuw [1], Hawkins and McLachlan [4], He and Fung [5], Croux and Dehon [2], Hubert and Van Driessen [6]. To obtain high breakdown these methods are based on high breakdown point estimators of lo...
متن کاملContextual constraints based linear discriminant analysis
Linear feature extraction methods such as LDA have achieved great success in pattern recognition and image processing area. For most existing methods, the image data is usually transformed into a vector representation and the contextual information among pixels is not exploited. However, image data distribute sparsely in high-dimension feature space and the dependence among neighboring pixels i...
متن کامل2D-LDA: A statistical linear discriminant analysis for image matrix
This paper proposes an innovative algorithm named 2D-LDA, which directly extracts the proper features from image matrices based on Fisher s Linear Discriminant Analysis. We experimentally compare 2D-LDA to other feature extraction methods, such as 2D-PCA, Eigenfaces and Fisherfaces. And 2D-LDA achieves the best performance. 2004 Elsevier B.V. All rights reserved.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Energy Procedia
سال: 2011
ISSN: 1876-6102
DOI: 10.1016/j.egypro.2011.12.512