Dissimilarity representation, Multidimensional scaling, Dimensionality reduction, Principal components analysis, Linear discriminant analysis,
Gisbrecht A, Hofmann D, Hammer B. Discriminative Dimensionality Reduction Mappings. In: Hollmén J, K...
Dimensionality reduction is an important issue when facing high-dimensional data. For supervised dim...
Information explosion has occurred in most of the sciences and researches due to advances in data co...
Abstract: We consider the problem of combining multiple dissimilarity representations via the Cartes...
Some state-of-the-art dimensionality reduction techniques are reviewed and investigated in this thes...
Hammer B, Gisbrecht A, Schulz A. Applications of discriminative dimensionality reduction. In: Proce...
Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional data, due to...
Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional data, due to...
The analysis of high-dimensional data often begins with the identification of lower dimensional subs...
For nearly a century, researchers have investigated and used mathematical techniques for reducing th...
Dimensionality reduction is the transformation of data from a high-dimensional space into a low-dime...
Thesis (Ph.D.)--University of Washington, 2022Dimensionality reduction is an essential topic in data...
Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) are the two popular techn...
Partial least squares discriminant analysis: A dimensionality reduction method to classify hyperspec...
A dimension reduction method in kernel discriminant analysis is presented, based on the concept of d...
Gisbrecht A, Hofmann D, Hammer B. Discriminative Dimensionality Reduction Mappings. In: Hollmén J, K...
Dimensionality reduction is an important issue when facing high-dimensional data. For supervised dim...
Information explosion has occurred in most of the sciences and researches due to advances in data co...
Abstract: We consider the problem of combining multiple dissimilarity representations via the Cartes...
Some state-of-the-art dimensionality reduction techniques are reviewed and investigated in this thes...
Hammer B, Gisbrecht A, Schulz A. Applications of discriminative dimensionality reduction. In: Proce...
Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional data, due to...
Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional data, due to...
The analysis of high-dimensional data often begins with the identification of lower dimensional subs...
For nearly a century, researchers have investigated and used mathematical techniques for reducing th...
Dimensionality reduction is the transformation of data from a high-dimensional space into a low-dime...
Thesis (Ph.D.)--University of Washington, 2022Dimensionality reduction is an essential topic in data...
Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) are the two popular techn...
Partial least squares discriminant analysis: A dimensionality reduction method to classify hyperspec...
A dimension reduction method in kernel discriminant analysis is presented, based on the concept of d...
Gisbrecht A, Hofmann D, Hammer B. Discriminative Dimensionality Reduction Mappings. In: Hollmén J, K...
Dimensionality reduction is an important issue when facing high-dimensional data. For supervised dim...
Information explosion has occurred in most of the sciences and researches due to advances in data co...
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