Add to ORKGDimension reduction transformations in discriminant analysis are introduced. Their properties, as well as sufficient conditions for their characterization, are studied. Special attention is given to the continuous case, of particular importance in applications. An effective data based dimension reduction algorithm is proposed and its behavior illustrated in a classification problem where the class conditional probability distributions are multivariate normal with different covariance matrice
Sufficient dimension reduction methods provide effective ways to visualize discriminant anal-ysis pr...
This dissertation is comprised of four chapters. In the first chapter, we define the concept of lin...
Dimensionality reduction is an important issue when facing high-dimensional data. For supervised dim...
Dimension reduction transformations in discriminant analysis are introduced. Their properties, as we...
A dimension reduction method in kernel discriminant analysis is presented, based on the concept of d...
In this paper, we consider a linear supervised dimension reduction method for classification setting...
One common objective of many multivariate techniques is to achieve a reduction in dimensionality whi...
Hammer B, Gisbrecht A, Schulz A. Applications of discriminative dimensionality reduction. In: Proce...
We present an algorithm for the reduction of dimensionality useful in statistical classification pro...
Abstract: We compare two linear dimension-reduction methods for statisti-cal discrimination in terms...
For nearly a century, researchers have investigated and used mathematical techniques for reducing th...
A dimension reduction method in kernel discriminant analysis is presented, based on the concept of d...
Abstract — In this paper, we consider a linear supervised dimension reduction method for classificat...
One common objective of many multivariate techniques is to achieve a reduction in dimensionality whi...
Gisbrecht A, Schulz A, Hammer B. Discriminative Dimensionality Reduction for the Visualization of Cl...
Sufficient dimension reduction methods provide effective ways to visualize discriminant anal-ysis pr...
This dissertation is comprised of four chapters. In the first chapter, we define the concept of lin...
Dimensionality reduction is an important issue when facing high-dimensional data. For supervised dim...
Dimension reduction transformations in discriminant analysis are introduced. Their properties, as we...
A dimension reduction method in kernel discriminant analysis is presented, based on the concept of d...
In this paper, we consider a linear supervised dimension reduction method for classification setting...
One common objective of many multivariate techniques is to achieve a reduction in dimensionality whi...
Hammer B, Gisbrecht A, Schulz A. Applications of discriminative dimensionality reduction. In: Proce...
We present an algorithm for the reduction of dimensionality useful in statistical classification pro...
Abstract: We compare two linear dimension-reduction methods for statisti-cal discrimination in terms...
For nearly a century, researchers have investigated and used mathematical techniques for reducing th...
A dimension reduction method in kernel discriminant analysis is presented, based on the concept of d...
Abstract — In this paper, we consider a linear supervised dimension reduction method for classificat...
One common objective of many multivariate techniques is to achieve a reduction in dimensionality whi...
Gisbrecht A, Schulz A, Hammer B. Discriminative Dimensionality Reduction for the Visualization of Cl...
Sufficient dimension reduction methods provide effective ways to visualize discriminant anal-ysis pr...
This dissertation is comprised of four chapters. In the first chapter, we define the concept of lin...
Dimensionality reduction is an important issue when facing high-dimensional data. For supervised dim...