Add to ORKGMethods of dimensionality reduction provide a way to understand and visualize the structure of complex data sets. This book describes the methods to reduce the dimensionality of numerical databases. For each method, the description starts from intuitive ideas, develops the mathematical details, and ends by outlining the algorithmic implementation
Dimensionality reduction is the conversion of high-dimensional data into a meaningful representation...
Schulz A, Gisbrecht A, Hammer B. Using Nonlinear Dimensionality Reduction to Visualize Classifiers. ...
This report discusses one paper for linear data dimensionality reduction, Eigenfaces, and two recent...
Dimensionality reduction techniques are outlined; their strengths and limitations are discussed. The...
Gisbrecht A, Hammer B. Data visualization by nonlinear dimensionality reduction. Wiley Interdiscipli...
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...
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 ...
Dimensionality reduction techniques aim at representing high dimensional data in a meaningful and lo...
Dimensionality reduction is an unsupervised task that allows high-dimensional data to be processed o...
Bunte K, Hammer B, Biehl M. Nonlinear dimension reduction and visualization of labeled data. In: Jia...
We describe an algorithm for nonlinear dimensionality reduction based on semidefinite programming ...
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...
Dimensionality reduction is the conversion of high-dimensional data into a meaningful representation...
Schulz A, Gisbrecht A, Hammer B. Using Nonlinear Dimensionality Reduction to Visualize Classifiers. ...
This report discusses one paper for linear data dimensionality reduction, Eigenfaces, and two recent...
Dimensionality reduction techniques are outlined; their strengths and limitations are discussed. The...
Gisbrecht A, Hammer B. Data visualization by nonlinear dimensionality reduction. Wiley Interdiscipli...
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...
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 ...
Dimensionality reduction techniques aim at representing high dimensional data in a meaningful and lo...
Dimensionality reduction is an unsupervised task that allows high-dimensional data to be processed o...
Bunte K, Hammer B, Biehl M. Nonlinear dimension reduction and visualization of labeled data. In: Jia...
We describe an algorithm for nonlinear dimensionality reduction based on semidefinite programming ...
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...
Dimensionality reduction is the conversion of high-dimensional data into a meaningful representation...
Schulz A, Gisbrecht A, Hammer B. Using Nonlinear Dimensionality Reduction to Visualize Classifiers. ...
This report discusses one paper for linear data dimensionality reduction, Eigenfaces, and two recent...