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155
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KDD
2006
ACM
156views Data Mining» more  KDD 2006»
16 years 4 months ago
Discovering significant OPSM subspace clusters in massive gene expression data
Order-preserving submatrixes (OPSMs) have been accepted as a biologically meaningful subspace cluster model, capturing the general tendency of gene expressions across a subset of ...
Byron J. Gao, Obi L. Griffith, Martin Ester, Steve...
167
Voted
JMLR
2006
124views more  JMLR 2006»
15 years 3 months ago
A Direct Method for Building Sparse Kernel Learning Algorithms
Many kernel learning algorithms, including support vector machines, result in a kernel machine, such as a kernel classifier, whose key component is a weight vector in a feature sp...
Mingrui Wu, Bernhard Schölkopf, Gökhan H...
156
Voted
ICIP
2008
IEEE
16 years 5 months ago
Subspace-based methods for image registration and super-resolution
Super-resolution algorithms combine multiple low resolution images into a single high resolution image. They have received a lot of attention recently in various application domai...
Patrick Vandewalle, Loïc Baboulaz, Pier Luigi...
VLDB
2007
ACM
174views Database» more  VLDB 2007»
16 years 3 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
JMLR
2002
137views more  JMLR 2002»
15 years 3 months ago
The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces
A central problem in learning is selection of an appropriate model. This is typically done by estimating the unknown generalization errors of a set of models to be selected from a...
Masashi Sugiyama, Klaus-Robert Müller