Robust kernel discriminant analysis and its application to feature extraction and recognition
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Summary
A novel subspace method called robust kernel discriminant analysis is proposed for dimensionality reduction, which aims at finding a low-dimensional space of high-dimensional data by solving the eigenvalue problem.
- Type
- article
- Published
- 2006-03-01
- Cited by
- 10
- References
- 13
- OpenAlex
- https://openalex.org/W1985617098
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:45533984
Keywords
Kernel Fisher discriminant analysis, Dimensionality reduction, Subspace topology, Pattern recognition (psychology), Linear discriminant analysis
References
- Learning with Kernels: support vector machines, regularization, optimization, and beyond
- Face recognition based on the uncorrelated discriminant transformation
- LDA/QR: an efficient and effective dimension reduction algorithm and its theoretical foundation
- An optimization criterion for generalized discriminant analysis on undersampled problems
- An efficient and effective method to solve kernel Fisher discriminant analysis
- Generalized Discriminant Analysis Using a Kernel Approach
- Uncorrelated discriminant vectors using a kernel method
- UCI Repository of machine learning databases
- Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
- Nonlinear Component Analysis as a Kernel Eigenvalue Problem
- Generalizing discriminant analysis using the generalized singular value decomposition
- Robust linear dimensionality reduction
Cited by
- Geometric margin domain description with instance-specific margins
- Etude et modélisation du comportement des gouttelettes de produits phytosanitaires sur les feuilles de vignes par imagerie ultra-rapide et analyse de texture
- Multiple kernels for generalised discriminant analysis
- Plant leaf roughness analysis by texture classification with generalized Fourier descriptors in a dimensionality reduction context
- Multiple-Camera-Based Gesture Recognition by MDA Method
- Bilinear Analysis for Kernel Selection and Nonlinear Feature Extraction
- Classification de textures par Descripteurs Généralisés de Fourier dans différents contextes de réduction de dimension
- Noise Robustness of a Texture Classification Protocol for Natural Leaf Roughness Characterisation
- A New Fuzzy Discri m inant Analysis Method
- A New Fuzzy Discriminant Analysis Method Horia F. Pop
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