Results 261 to 270 of about 3,322,529 (303)

Dimensionality reduction and generalization

Proceedings of the 24th international conference on Machine learning, 2007
In this paper we investigate the regularization property of Kernel Principal Component Analysis (KPCA), by studying its application as a preprocessing step to supervised learning problems. We show that performing KPCA and then ordinary least squares on the projected data, a procedure known as kernel principal component regression (KPCR), is equivalent ...
MOSCI, SOFIA, ROSASCO, LORENZO, VERRI, ALESSANDRO   +2 more
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Local dimensionality reduction

Computational Statistics, 1999
Different methods of dimensionality reduction such as principal components and Fisher's linear discriminant (FLD) are considered. The authors are interested in local versions of these methods based on normal mixtures and nearest neighbors approach. The Iterated Nearest Neighbor FLD (INN) is an example of such methods. Suppose, that a training sample of
David J. Marchette, Wendy L. Poston
openaire   +3 more sources

Dimensionality Reduction for Classification

2008
We 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, Steven De Bruyne, Emilio Carrizosa   +2 more
openaire   +2 more sources

Robust linear dimensionality reduction

IEEE Transactions on Visualization and Computer Graphics, 2004
We present a novel family of data-driven linear transformations, aimed at finding low-dimensional embeddings of multivariate data, in a way that optimally preserves the structure of the data. The well-studied PCA and Fisher's LDA are shown to be special members in this family of transformations, and we demonstrate how to generalize these two methods ...
Yehuda Koren, Liran Carmel
openaire   +3 more sources

SEMISUPERVISED MULTIMODAL DIMENSIONALITY REDUCTION

Computational Intelligence, 2012
The 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, Tommy W. S. Chow, Ning Ye   +2 more
openaire   +3 more sources

Sufficient dimensionality reduction

J. Mach. Learn. Res., 2003
Summary: 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
openaire   +2 more sources

Neighborhood Selection for Dimensionality Reduction

2015
Though 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
openaire   +3 more sources

Bayesian Supervised Dimensionality Reduction

IEEE Transactions on Cybernetics, 2013
Dimensionality 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 ...
openaire   +5 more sources

Diffeomorphic Dimensionality Reduction.

2009
This 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.
openaire   +2 more sources

Feature dimensionality reduction: a review

Complex & Intelligent Systems, 2022
Weikuan Jia, Sujuan Hou, Jian Lian
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