Results 271 to 280 of about 9,586,369 (306)

Robust dimension reduction

WIREs Computational Statistics, 2014
Information in the data often has far fewer degrees of freedom than the number of variables encoding the data. Dimensionality reduction attempts to reduce the number of variables used to describe the data. In this article, we shall survey some dimension reduction techniques that are robust.
Chenouri, Shojaeddin, Liang, Jiaxi, Small, Christopher G.   +2 more
openaire   +2 more sources

Double Shrinking Sparse Dimension Reduction

IEEE Transactions on Image Processing, 2013
Learning tasks such as classification and clustering usually perform better and cost less (time and space) on compressed representations than on the original data. Previous works mainly compress data via dimension reduction. In this paper, we propose "double shrinking" to compress image data on both dimensionality and cardinality via building either ...
Tianyi, Zhou, Dacheng, Tao
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Sufficient Dimension Reduction and Kernel Dimension Reduction

2023
Benyamin Ghojogh, Mark Crowley, Fakhri Karray, Ali Ghodsi   +3 more
openaire   +1 more source

Model Dimension Reduction

2015
A real system, especially a distributed parameter system, may havehigh or even infinite dimensions of freedom (DOF). When the DOF ofa model is too high, all inversion methods that we have learnedbecome inefficient and the inverse problem becomes unsolvablebecause of data and computational limitations.
Ne-Zheng Sun, Alexander Sun
openaire   +1 more source

Dimension reduction techniques

2016
Large datasets, as well as data consisting of a large number of features, present computational problems in the training of predictive models. In this chapter we discuss several useful techniques for reducing the dimension of a given dataset, that is reducing the number of data points or number of features, often employed in order to make predictive ...
openaire   +1 more source

Dimension Reduction and Its Applications

2003
This chapter is motivated by our attempt to answer pertinent questions concerning a number of real data sets, some of which are listed below.
Cizek, P., Xia, Y.
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Dimension Reduction

2008
Shashi Shekhar, Hui Xiong
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Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach

IEEE Transactions on Geoscience and Remote Sensing, 2016
Wenzhi Zhao, S. Du
semanticscholar   +1 more source

Dimension Reduction

2023
Frank Emmert-Streib, Salissou Moutari, Matthias Dehmer   +2 more
openaire   +1 more source

Sliced Inverse Regression for Dimension Reduction

, 1991
Ker-Chau Li
semanticscholar   +1 more source