Add to ORKGFor nearly a century, researchers have investigated and used mathematical techniques for reducing the dimensionality of vector valued data used to characterize categorical data with the goal of preserving “information” or discriminability of the different categories in the reduced dimensionality data
We are interested in using the goal of making predictions to influence dimensionality reduction proc...
this dissertation unsupervised statistical pattern recognition is examined. It is divided in two par...
This thesis centers around dimensionality reduction and its usage on landmark-type data which are of...
For nearly a century, researchers have investigated and used mathematical techniques for reducing th...
Hammer B, Gisbrecht A, Schulz A. Applications of discriminative dimensionality reduction. In: Proce...
Gisbrecht A, Schulz A, Hammer B. Discriminative Dimensionality Reduction for the Visualization of Cl...
For nearly a century, researchers have investigated and used mathematical techniques for reducing th...
The use of dimensionality reduction techniques is a keystone for analyzing and interpreting high dim...
For knowledge gaining the dimensionality reduction is a significant technique. It has been observed ...
Abstract- Classification is undoubtedly gaining major importance in the fields of machine learning, ...
Dimensionality reduction is the transformation of data from a high-dimensional space into a low-dime...
When data objects that are the subject of analysis using machine learning techniques are described b...
When data objects that are the subject of analysis using machine learning techniques are described b...
Abstract — Data dimensionality refers to the number of variables that are measured on each observat...
We investigate the effects of dimensionality reduction using different techniques and different dime...
We are interested in using the goal of making predictions to influence dimensionality reduction proc...
this dissertation unsupervised statistical pattern recognition is examined. It is divided in two par...
This thesis centers around dimensionality reduction and its usage on landmark-type data which are of...
For nearly a century, researchers have investigated and used mathematical techniques for reducing th...
Hammer B, Gisbrecht A, Schulz A. Applications of discriminative dimensionality reduction. In: Proce...
Gisbrecht A, Schulz A, Hammer B. Discriminative Dimensionality Reduction for the Visualization of Cl...
For nearly a century, researchers have investigated and used mathematical techniques for reducing th...
The use of dimensionality reduction techniques is a keystone for analyzing and interpreting high dim...
For knowledge gaining the dimensionality reduction is a significant technique. It has been observed ...
Abstract- Classification is undoubtedly gaining major importance in the fields of machine learning, ...
Dimensionality reduction is the transformation of data from a high-dimensional space into a low-dime...
When data objects that are the subject of analysis using machine learning techniques are described b...
When data objects that are the subject of analysis using machine learning techniques are described b...
Abstract — Data dimensionality refers to the number of variables that are measured on each observat...
We investigate the effects of dimensionality reduction using different techniques and different dime...
We are interested in using the goal of making predictions to influence dimensionality reduction proc...
this dissertation unsupervised statistical pattern recognition is examined. It is divided in two par...
This thesis centers around dimensionality reduction and its usage on landmark-type data which are of...