WebLinear Discriminant Analysis (LDA) or Fischer Discriminants (Duda et al., 2001) is a common technique used for dimensionality reduction and classification. LDA provides class separability by drawing a decision region between the different classes. LDA tries to maximize the ratio of the between-class variance and the within-class variance. WebJan 1, 2005 · In this paper, we propose a novel robust linear discriminant analysis method based on the L1,2-norm ratio minimization. Minimizing the L1,2-norm ratio is a much more challenging problem than the ...
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WebMay 20, 2024 · Inspired by two recent linear discriminant methods: robust sparse linear discriminant analysis (RSLDA) and inter-class sparsity-based discriminative least square regression (ICS_DLSR), we propose a unifying criterion that is able to retain the advantages of these two powerful methods. WebSep 1, 2024 · Recently, L1-norm distance measure based Linear Discriminant Analysis (LDA) techniques have been shown to be robust against outliers. However, these methods have no guarantee of obtaining a satisfactory-enough performance due to the insufficient robustness of L1-norm measure. the old rectory ducklington
Robust Fisher Discriminant Analysis. - ResearchGate
WebRobust and Sparse Linear Discriminant Analysis via an Alternating Direction Method of Multipliers IEEE Trans Neural Netw Learn Syst. 2024 Mar;31 (3):915-926. doi: 10.1109/TNNLS.2024.2910991. Epub 2024 May 9. Authors Chun-Na Li , Yuan-Hai Shao , Wotao Yin , Ming-Zeng Liu PMID: 31094696 DOI: 10.1109/TNNLS.2024.2910991 WebMar 4, 2024 · In this study, a novel robust and efficient feature selection method, called FS-VLDA-L 2,1 (feature selection based on variant of linear discriminant analysis and L 2,1 … WebJul 22, 2024 · Abstract: Linear discriminant analysis technique is an effective strategy to solve the long-standing issue, i.e., the “curse of dimensionality” that brings many obstacles on high-dimensional data storage and analysis. the old rectory fyfield