Add to ORKGAbstract: Feature extraction and dimensionality reduction are impor-tant tasks in many fields of science dealing with signal processing and analysis. The relevance of these techniques is increasing as current sensory devices are developed with ever higher resolution, and problems involving multimodal data sources become more common. A plethora of feature extraction methods are available in the literature collectively grouped under the field of Multivariate Analysis (MVA). This paper provides a uniform treatment of several methods: Principa
Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) are the two popular techn...
This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis,...
This research book provides a comprehensive overview of the state-of-the-art subspace learning metho...
This paper provides a new insight into unsupervised feature extraction techniques based on kernel su...
190 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008.To demonstrate the effectiven...
Kernel based nonlinear Feature Extraction (KFE) or dimensionality reduction is a widely used pre-pro...
Kernel methods are nonparametric feature extraction techniques that attempt to boost the learning ca...
Kernel based nonlinear Feature Extraction (KFE) or dimensionality reduction is a widely used preproc...
Dimensionality reduction methods play a big role within the modern machine learning techniques, and ...
Kernel based nonlinear Feature Extraction (KFE) or dimensionality reduction is a widely used pre-pro...
Dimensionality reduction methods play a big role within the modern machine learning techniques, and ...
Abstract—Multilinear subspace analysis (MSA) is a promising methodology for pattern recognition prob...
We propose a novel method of dimensionality reduction for supervised learning. Given a regression or...
Using the kernel trick idea and the kernels-as-features idea, we can construct two kinds of nonlinea...
We study the use of kernel subspace methods that learn low-dimensional subspace representations for ...
Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) are the two popular techn...
This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis,...
This research book provides a comprehensive overview of the state-of-the-art subspace learning metho...
This paper provides a new insight into unsupervised feature extraction techniques based on kernel su...
190 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008.To demonstrate the effectiven...
Kernel based nonlinear Feature Extraction (KFE) or dimensionality reduction is a widely used pre-pro...
Kernel methods are nonparametric feature extraction techniques that attempt to boost the learning ca...
Kernel based nonlinear Feature Extraction (KFE) or dimensionality reduction is a widely used preproc...
Dimensionality reduction methods play a big role within the modern machine learning techniques, and ...
Kernel based nonlinear Feature Extraction (KFE) or dimensionality reduction is a widely used pre-pro...
Dimensionality reduction methods play a big role within the modern machine learning techniques, and ...
Abstract—Multilinear subspace analysis (MSA) is a promising methodology for pattern recognition prob...
We propose a novel method of dimensionality reduction for supervised learning. Given a regression or...
Using the kernel trick idea and the kernels-as-features idea, we can construct two kinds of nonlinea...
We study the use of kernel subspace methods that learn low-dimensional subspace representations for ...
Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) are the two popular techn...
This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis,...
This research book provides a comprehensive overview of the state-of-the-art subspace learning metho...