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ICDE
2007
IEEE
228views Database» more  ICDE 2007»
10 years 4 months ago
A General Cost Model for Dimensionality Reduction in High Dimensional Spaces
Similarity search usually encounters a serious problem in the high dimensional space, known as the “curse of dimensionality”. In order to speed up the retrieval efficiency, p...
Xiang Lian, Lei Chen 0002
ICCV
2007
IEEE
10 years 4 months ago
Laplacian PCA and Its Applications
Dimensionality reduction plays a fundamental role in data processing, for which principal component analysis (PCA) is widely used. In this paper, we develop the Laplacian PCA (LPC...
Deli Zhao, Zhouchen Lin, Xiaoou Tang
ICASSP
2008
IEEE
10 years 5 months ago
A study of using locality preserving projections for feature extraction in speech recognition
This paper presents a new approach to feature analysis in automatic speech recognition (ASR) based on locality preserving projections (LPP). LPP is a manifold based dimensionality...
Yun Tang, Richard Rose
COMPGEOM
2009
ACM
10 years 5 months ago
Persistent cohomology and circular coordinates
Nonlinear dimensionality reduction (NLDR) algorithms such as Isomap, LLE and Laplacian Eigenmaps address the problem of representing high-dimensional nonlinear data in terms of lo...
Vin de Silva, Mikael Vejdemo-Johansson

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Marwan A TorkiStudent, PhD
Rutgers University
Marwan A Torki
Marwan Torki is a Ph. D. student in computer science department at Rutgers University. He took his M.Sc. and B.Sc. in computer science from department of computer science, faculty ...
ICASSP
2009
IEEE
10 years 5 months ago
An information geometric approach to supervised dimensionality reduction
Due to the curse of dimensionality, high-dimensional data is often pre-processed with some form of dimensionality reduction for the classification task. Many common methods of su...
Kevin M. Carter, Raviv Raich, Alfred O. Hero
CVPR
2010
IEEE
10 years 6 months ago
Parametric Dimensionality Reduction by Unsupervised Regression
We introduce a parametric version (pDRUR) of the recently proposed Dimensionality Reduction by Unsupervised Regression algorithm. pDRUR alternately minimizes reconstruction error ...
Miguel Carreira-perpinan, Zhengdong Lu
CVPR
2010
IEEE
10 years 6 months ago
Sufficient Dimensionality Reduction for Visual Sequence Classification
When classifying high-dimensional sequence data, traditional methods (e.g., HMMs, CRFs) may require large amounts of training data to avoid overfitting. In such cases dimensional...
Alex Shyr, Raquel Urtasun, Michael Jordan

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zhomlynnStudent, PhD
USTC
zhomlynn
PODS
2001
ACM
190views Database» more  PODS 2001»
10 years 10 months ago
On the Effects of Dimensionality Reduction on High Dimensional Similarity Search
The dimensionality curse has profound e ects on the effectiveness of high-dimensional similarity indexing from the performance perspective. One of the well known techniques for im...
Charu C. Aggarwal
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