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» On the Optimality of the Dimensionality Reduction Method
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CGO
2007
IEEE
16 years 13 days ago
Iterative Optimization in the Polyhedral Model: Part I, One-Dimensional Time
Emerging microprocessors offer unprecedented parallel computing capabilities and deeper memory hierarchies, increasing the importance of loop transformations in optimizing compile...
Louis-Noël Pouchet, Cédric Bastoul, Al...
CEC
2007
IEEE
16 years 13 days ago
Improving hypervolume-based multiobjective evolutionary algorithms by using objective reduction methods
— Hypervolume based multiobjective evolutionary algorithms (MOEA) nowadays seem to be the first choice when handling multiobjective optimization problems with many, i.e., at lea...
Dimo Brockhoff, Eckart Zitzler
SDM
2009
SIAM
152views Data Mining» more  SDM 2009»
16 years 3 months ago
Non-negative Matrix Factorization, Convexity and Isometry.
In this paper we explore avenues for improving the reliability of dimensionality reduction methods such as Non-Negative Matrix Factorization (NMF) as interpretive exploratory data...
Nikolaos Vasiloglou, Alexander G. Gray, David V. A...
ICIP
2004
IEEE
16 years 7 months ago
Estimation of mixtures of probabilistic pca with stochastic em for the 3d biplanar reconstruction of scoliotic rib cage
In this paper, we present a robust method for estimating the model parameters in a mixture of probabilistic principal component analyzers. This method is based on the Stochastic v...
François Destrempes, Jacques A. de Guise, M...
SDM
2004
SIAM
162views Data Mining» more  SDM 2004»
15 years 7 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...