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» Information Preserving Dimensionality Reduction
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CVPR
2008
IEEE
14 years 11 months ago
Dimensionality reduction using covariance operator inverse regression
We consider the task of dimensionality reduction for regression (DRR) whose goal is to find a low dimensional representation of input covariates, while preserving the statistical ...
Minyoung Kim, Vladimir Pavlovic
SIGMOD
2002
ACM
246views Database» more  SIGMOD 2002»
14 years 10 months ago
Hierarchical subspace sampling: a unified framework for high dimensional data reduction, selectivity estimation and nearest neig
With the increased abilities for automated data collection made possible by modern technology, the typical sizes of data collections have continued to grow in recent years. In suc...
Charu C. Aggarwal
WEBI
2010
Springer
13 years 7 months ago
DSP: Robust Semi-supervised Dimensionality Reduction Using Dual Subspace Projections
High-dimensional data usually incur learning deficiencies and computational difficulties. We present a novel semi-supervised dimensionality reduction technique that embeds high-dim...
Su Yan, Sofien Bouaziz, Dongwon Lee
IJON
2010
121views more  IJON 2010»
13 years 7 months ago
Sample-dependent graph construction with application to dimensionality reduction
Graph construction plays a key role on learning algorithms based on graph Laplacian. However, the traditional graph construction approaches of -neighborhood and k-nearest-neighbor...
Bo Yang, Songcan Chen
ICIP
2006
IEEE
14 years 11 months ago
Wavelet Principal Component Analysis and its Application to Hyperspectral Images
We investigate reducing the dimensionality of image sets by using principal component analysis on wavelet coefficients to maximize edge energy in the reduced dimension images. Lar...
Maya R. Gupta, Nathaniel P. Jacobson