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Dimensionality Reduction for Classification
2008We investigate the effects of dimensionality reduction using different techniques and different dimensions on six two-class data sets with numerical attributes as pre-processing for two classification algorithms. Besides reducing the dimensionality with the use of principal components and linear discriminants, we also introduce four new techniques ...
Frank Plastria +2 more
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SEMISUPERVISED MULTIMODAL DIMENSIONALITY REDUCTION
Computational Intelligence, 2012The problem of learning from both labeled and unlabeled data is considered. In this paper, we present a novel semisupervised multimodal dimensionality reduction (SSMDR) algorithm for feature reduction and extraction. SSMDR can preserve the local and multimodal structures of labeled and unlabeled samples. As a result, data pairs in the close vicinity of
Zhao Zhang 0001 +2 more
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Bayesian Supervised Dimensionality Reduction
IEEE Transactions on Cybernetics, 2013Dimensionality reduction is commonly used as a preprocessing step before training a supervised learner. However, coupled training of dimensionality reduction and supervised learning steps may improve the prediction performance. In this paper, we introduce a simple and novel Bayesian supervised dimensionality reduction method that combines linear ...
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Diffeomorphic Dimensionality Reduction.
2009This paper introduces a new approach to constructing meaningful lower dimensional representations of sets of data points. We argue that constraining the mapping between the high and low dimensional spaces to be a diffeomorphism is a natural way of ensuring that pairwise distances are approximately preserved.
Walder, C., Schölkopf, B.
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Sufficient dimensionality reduction
J. Mach. Learn. Res., 2003Summary: Dimensionality reduction of empirical co-occurrence data is a fundamental problem in unsupervised learning. It is also a well studied problem in statistics known as the analysis of cross-classified data. One principled approach to this problem is to represent the data in low dimension with minimal loss of (mutual) information contained in the ...
Amir Globerson, Naftali Tishby
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Neighborhood Selection for Dimensionality Reduction
2015Though a great deal of research work has been devoted to the development of dimensionality reduction algorithms, the problem is still open. The most recent and effective techniques, assuming datasets drawn from an underlying low dimensional manifold embedded into an high dimensional space, look for “small enough” neighborhoods which should represent ...
P. Campadelli, E. Casiraghi, C. Ceruti
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Feature dimensionality reduction: a review
Complex & Intelligent Systems, 2022Weikuan Jia, Meili Sun, Jian Lian
exaly
Explaining dimensionality reduction results using Shapley values
Expert Systems With Applications, 2021Wilson E Marcilio-Jr +1 more
exaly
2019
Dimensionality reduction is a hot research topic in data analysis today. Thanks to the advances in high-performance computing technologies and in the engineering eld, we entered in the so-called big-data era and an enormous quantity of data is available in every scientificc area, ranging from social networking, economy and politics to e-health and life
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Dimensionality reduction is a hot research topic in data analysis today. Thanks to the advances in high-performance computing technologies and in the engineering eld, we entered in the so-called big-data era and an enormous quantity of data is available in every scientificc area, ranging from social networking, economy and politics to e-health and life
openaire +3 more sources