Add to ORKGDimensionality reduction techniques aim at representing high dimensional data in a meaningful and lower-dimensional space, improving the human comprehension and interpretation of data. In recent years, newer nonlinear techniques have been proposed in order to address the limitation of linear techniques. This paper presents a study of the stability of some of these dimensionality reduction techniques, analyzing their behavior under changes in the parameters and the data. The performances of these techniques are investigated on artificial datasets. The paper presents these results by identifying the weaknesses of each technique, and suggests some data-processing tasks to improve the stability
Data analysis in management applications often requires to handle data with a large number of varia...
For nearly a century, researchers have investigated and used mathematical techniques for reducing th...
For knowledge gaining the dimensionality reduction is a significant technique. It has been observed ...
The analysis of the big volumes of data requires efficient and robust dimension reduction techniques...
The analysis of the big volumes of data requires efficient and robust dimension reduction techniques...
Dimensionality reduction techniques are outlined; their strengths and limitations are discussed. The...
The use of dimensionality reduction techniques is a keystone for analyzing and interpreting high dim...
The visual interpretation of data is an essential step to guide any further processing or decision m...
Abstract — Data dimensionality refers to the number of variables that are measured on each observat...
Machine learning methods are used to build models for classification and regression tasks, among oth...
Methods of dimensionality reduction provide a way to understand and visualize the structure of compl...
Dimensionality reduction is the transformation of data from a high-dimensional space into a low-dime...
Nonlinear dimensionality reduction (NLDR) methods have become useful tools for practitioners who are...
The problem of nonlinear dimensionality reduction is considered. We focus on problems where prior in...
In recent years, the huge expansion of digital technologies has vastly increased the volume of data ...
Data analysis in management applications often requires to handle data with a large number of varia...
For nearly a century, researchers have investigated and used mathematical techniques for reducing th...
For knowledge gaining the dimensionality reduction is a significant technique. It has been observed ...
The analysis of the big volumes of data requires efficient and robust dimension reduction techniques...
The analysis of the big volumes of data requires efficient and robust dimension reduction techniques...
Dimensionality reduction techniques are outlined; their strengths and limitations are discussed. The...
The use of dimensionality reduction techniques is a keystone for analyzing and interpreting high dim...
The visual interpretation of data is an essential step to guide any further processing or decision m...
Abstract — Data dimensionality refers to the number of variables that are measured on each observat...
Machine learning methods are used to build models for classification and regression tasks, among oth...
Methods of dimensionality reduction provide a way to understand and visualize the structure of compl...
Dimensionality reduction is the transformation of data from a high-dimensional space into a low-dime...
Nonlinear dimensionality reduction (NLDR) methods have become useful tools for practitioners who are...
The problem of nonlinear dimensionality reduction is considered. We focus on problems where prior in...
In recent years, the huge expansion of digital technologies has vastly increased the volume of data ...
Data analysis in management applications often requires to handle data with a large number of varia...
For nearly a century, researchers have investigated and used mathematical techniques for reducing th...
For knowledge gaining the dimensionality reduction is a significant technique. It has been observed ...