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» Reducing the Complexity of Reductions
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NIPS
2008
15 years 8 months ago
Dimensionality Reduction for Data in Multiple Feature Representations
In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. These representa...
Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh
ECCV
2006
Springer
16 years 8 months ago
Riemannian Manifold Learning for Nonlinear Dimensionality Reduction
In recent years, nonlinear dimensionality reduction (NLDR) techniques have attracted much attention in visual perception and many other areas of science. We propose an efficient al...
Tony Lin, Hongbin Zha, Sang Uk Lee
ISBI
2004
IEEE
16 years 7 months ago
Nonlinear Dimension Reduction of fMRI Data: The Laplacian Embedding Approach
In this paper, we introduce the use of nonlinear dimension reduction for the analysis of functional neuroimaging datasets. Using a Laplacian Embedding approach, we show the power ...
Olivier D. Faugeras, Bertrand Thirion
ICCD
2002
IEEE
93views Hardware» more  ICCD 2002»
16 years 3 months ago
Impact of Scaling on the Effectiveness of Dynamic Power Reduction Schemes
Power is considered to be the major limiter to the design of more faster and complex processors in the near future. In order to address this challenge, a combination of process, c...
David Duarte, Narayanan Vijaykrishnan, Mary Jane I...
ASIACRYPT
2000
Springer
15 years 11 months ago
Concrete Security Characterizations of PRFs and PRPs: Reductions and Applications
Abstract. We investigate several alternate characterizations of pseudorandom functions (PRFs) and pseudorandom permutations (PRPs) in a concrete security setting. By analyzing the ...
Anand Desai, Sara K. Miner