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» A Tensor Approximation Approach to Dimensionality Reduction
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CIKM
2003
Springer
15 years 7 months ago
Dimensionality reduction using magnitude and shape approximations
High dimensional data sets are encountered in many modern database applications. The usual approach is to construct a summary of the data set through a lossy compression technique...
Ümit Y. Ogras, Hakan Ferhatosmanoglu
112
Voted
ICCV
2009
IEEE
14 years 11 months ago
Local distance functions: A taxonomy, new algorithms, and an evaluation
We present a taxonomy for local distance functions where most existing algorithms can be regarded as approximations of the geodesic distance defined by a metric tensor. We categor...
Deva Ramanan, Simon Baker
MM
2005
ACM
122views Multimedia» more  MM 2005»
15 years 7 months ago
Image clustering with tensor representation
We consider the problem of image representation and clustering. Traditionally, an n1 × n2 image is represented by a vector in the Euclidean space Rn1×n2 . Some learning algorith...
Xiaofei He, Deng Cai, Haifeng Liu, Jiawei Han
TIT
2008
141views more  TIT 2008»
15 years 1 months ago
Dimensionality Reduction for Distributed Estimation in the Infinite Dimensional Regime
Distributed estimation of an unknown signal is a common task in sensor networks. The scenario usually envisioned consists of several nodes, each making an observation correlated wi...
Olivier Roy, Martin Vetterli
NIPS
2008
15 years 3 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