ICDE
2007
IEEE
13 years 10 months ago
2007
IEEE
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...
ICCV
2007
IEEE
13 years 10 months ago
2007
IEEE
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...
ICASSP
2008
IEEE
13 years 10 months ago
2008
IEEE
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...
COMPGEOM
2009
ACM
13 years 10 months ago
2009
ACM
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...
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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
13 years 11 months ago
2009
IEEE
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...
CVPR
2010
IEEE
14 years 15 hour ago
2010
IEEE
We introduce a parametric version (pDRUR) of the recently proposed Dimensionality Reduction by Unsupervised Regression algorithm. pDRUR alternately minimizes reconstruction error ...
CVPR
2010
IEEE
14 years 13 days ago
2010
IEEE
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...
PODS
2001
ACM
14 years 4 months ago
2001
ACM
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...