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» Denoising using local projective subspace methods
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VLDB
2007
ACM
174views Database» more  VLDB 2007»
14 years 9 months ago
An adaptive and dynamic dimensionality reduction method for high-dimensional indexing
Abstract The notorious "dimensionality curse" is a wellknown phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approa...
Heng Tao Shen, Xiaofang Zhou, Aoying Zhou
ECCV
2006
Springer
14 years 10 months ago
Random Walks, Constrained Multiple Hypothesis Testing and Image Enhancement
Image restoration is a keen problem of low level vision. In this paper, we propose a novel - assumption-free on the noise model - technique based on random walks for image enhancem...
Noura Azzabou, Nikos Paragios, Frederic Guichard
NIPS
2008
13 years 10 months ago
Diffeomorphic Dimensionality Reduction
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 a...
Christian Walder, Bernhard Schölkopf
SDM
2004
SIAM
162views Data Mining» more  SDM 2004»
13 years 10 months ago
Subspace Clustering of High Dimensional Data
Clustering suffers from the curse of dimensionality, and similarity functions that use all input features with equal relevance may not be effective. We introduce an algorithm that...
Carlotta Domeniconi, Dimitris Papadopoulos, Dimitr...
ICIP
2005
IEEE
14 years 10 months ago
Super-resolution from highly undersampled images
Aliasing artifacts in images are visually very disturbing. Therefore, most imaging devices apply a low-pass filter before sampling. This removes all aliasing from the image, but i...
Luciano Sbaiz, Martin Vetterli, Patrick Vandewalle...