Add to ORKGA dimension reduction method in kernel discriminant analysis is presented, based on the concept of dimension reduction subspace. Examples of application are discussed
The talk will start with a short tutorial on kernel methods in machine learning. Following this, we ...
This article proposes a novel approach to linear dimension reduction for regression using nonparamet...
AbstractThe concept of quadratic subspace is introduced as a helpful tool for dimension reduction in...
A dimension reduction method in kernel discriminant analysis is presented, based on the concept of d...
A dimension reduction method in kernel discriminant analysis is presented, based on the concept of d...
Dimension reduction transformations in discriminant analysis are introduced. Their properties, as we...
Abstract. Nonparametric regression is a powerful tool to estimate nonlinear relations between some p...
In Linear Discriminant Analysis (LDA), a dimension reducing linear transformation is found in order...
We study the use of kernel subspace methods that learn low-dimensional subspace representations for ...
Nonparametric regression is a powerful tool to estimate nonlinear relations between some predictors ...
Abstract—Linear and kernel discriminant analyses are popular approaches for supervised dimensionalit...
We study the use of kernel subspace methods for learning low-dimensional representations for classif...
Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) are the two popular techn...
Schulz A, Hammer B. Discriminative Dimensionality Reduction in Kernel Space. In: ESANN2016 Proceedi...
Hammer B, Gisbrecht A, Schulz A. Applications of discriminative dimensionality reduction. In: Proce...
The talk will start with a short tutorial on kernel methods in machine learning. Following this, we ...
This article proposes a novel approach to linear dimension reduction for regression using nonparamet...
AbstractThe concept of quadratic subspace is introduced as a helpful tool for dimension reduction in...
A dimension reduction method in kernel discriminant analysis is presented, based on the concept of d...
A dimension reduction method in kernel discriminant analysis is presented, based on the concept of d...
Dimension reduction transformations in discriminant analysis are introduced. Their properties, as we...
Abstract. Nonparametric regression is a powerful tool to estimate nonlinear relations between some p...
In Linear Discriminant Analysis (LDA), a dimension reducing linear transformation is found in order...
We study the use of kernel subspace methods that learn low-dimensional subspace representations for ...
Nonparametric regression is a powerful tool to estimate nonlinear relations between some predictors ...
Abstract—Linear and kernel discriminant analyses are popular approaches for supervised dimensionalit...
We study the use of kernel subspace methods for learning low-dimensional representations for classif...
Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) are the two popular techn...
Schulz A, Hammer B. Discriminative Dimensionality Reduction in Kernel Space. In: ESANN2016 Proceedi...
Hammer B, Gisbrecht A, Schulz A. Applications of discriminative dimensionality reduction. In: Proce...
The talk will start with a short tutorial on kernel methods in machine learning. Following this, we ...
This article proposes a novel approach to linear dimension reduction for regression using nonparamet...
AbstractThe concept of quadratic subspace is introduced as a helpful tool for dimension reduction in...