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JMLR
2010
144views more  JMLR 2010»
13 years 2 months ago
Practical Approaches to Principal Component Analysis in the Presence of Missing Values
Principal component analysis (PCA) is a classical data analysis technique that finds linear transformations of data that retain the maximal amount of variance. We study a case whe...
Alexander Ilin, Tapani Raiko
SLS
2007
Springer
111views Algorithms» more  SLS 2007»
14 years 1 months ago
Mixed Models for the Analysis of Local Search Components
We consider a possible scenario of experimental analysis on heuristics for optimization: identifying the contribution of local search components when algorithms are evaluated on th...
Jørgen Bang-Jensen, Marco Chiarandini, Yuri...
ICDM
2006
IEEE
225views Data Mining» more  ICDM 2006»
14 years 1 months ago
Adaptive Kernel Principal Component Analysis with Unsupervised Learning of Kernels
Choosing an appropriate kernel is one of the key problems in kernel-based methods. Most existing kernel selection methods require that the class labels of the training examples ar...
Daoqiang Zhang, Zhi-Hua Zhou, Songcan Chen
ICA
2007
Springer
14 years 1 months ago
Sparse Component Analysis in Presence of Noise Using an Iterative EM-MAP Algorithm
Abstract. In this paper, a new algorithm for source recovery in underdetermined Sparse Component Analysis (SCA) or atomic decomposition on over-complete dictionaries is presented i...
Hadi Zayyani, Massoud Babaie-Zadeh, G. Hosein Mohi...
ISQED
2000
IEEE
117views Hardware» more  ISQED 2000»
13 years 12 months ago
Realistic Worst-Case Modeling by Performance Level Principal Component Analysis
A new algorithm to determine the number and value of realistic worst-case models for the performance of module library components is presented in this paper. The proposed algorith...
Alessandra Nardi, Andrea Neviani, Carlo Guardiani