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ISCI
2008
130views more  ISCI 2008»
9 years 3 months ago
Unified eigen analysis on multivariate Gaussian based estimation of distribution algorithms
Multivariate Gaussian models are widely adopted in continuous Estimation of Distribution Algorithms (EDAs), and covariance matrix plays the essential role in guiding the evolution...
Weishan Dong, Xin Yao
IJHPCA
2008
104views more  IJHPCA 2008»
9 years 3 months ago
Low-Complexity Principal Component Analysis for Hyperspectral Image Compression
Principal component analysis (PCA) is an effective tool for spectral decorrelation of hyperspectral imagery, and PCA-based spectral transforms have been employed successfully in co...
Qian Du, James E. Fowler
AUTOMATICA
2008
69views more  AUTOMATICA 2008»
9 years 3 months ago
Optimal sensor locations for nonparametric identification of viscoelastic materials
: The problem of optimal sensor locations in nonparametric identification of viscoelastic materials is considered. Different criteria of the covariance matrix, connected to A- and ...
Agnes Rensfelt, Saed Mousavi, Magnus Mossberg, Tor...
ICASSP
2010
IEEE
9 years 3 months ago
Distributed bearing estimation via matrix completion
We consider bearing estimation of multiple narrow-band plane waves impinging on an array of sensors. For this problem, bearing estimation algorithms such as minimum variance disto...
Andrew Waters, Volkan Cevher
NIPS
2004
9 years 5 months ago
A Direct Formulation for Sparse PCA Using Semidefinite Programming
We examine the problem of approximating, in the Frobenius-norm sense, a positive, semidefinite symmetric matrix by a rank-one matrix, with an upper bound on the cardinality of its...
Alexandre d'Aspremont, Laurent El Ghaoui, Michael ...
NIPS
2004
9 years 5 months ago
Dependent Gaussian Processes
Gaussian processes are usually parameterised in terms of their covariance functions. However, this makes it difficult to deal with multiple outputs, because ensuring that the cova...
Phillip Boyle, Marcus R. Frean
NIPS
2007
9 years 5 months ago
Multi-task Gaussian Process Prediction
In this paper we investigate multi-task learning in the context of Gaussian Processes (GP). We propose a model that learns a shared covariance function on input-dependent features...
Edwin V. Bonilla, Kian Ming Chai, Christopher K. I...
MVA
2007
127views Computer Vision» more  MVA 2007»
9 years 5 months ago
Appearance Manifold with Embedded Covariance Matrix for Robust 3D Object Recognition
We propose use of an appearance manifold with embedded covariance matrix as a technique for recognizing 3D objects from images that are influenced by geometric and quality-degrade...
Lina, Tomokazu Takahashi, Ichiro Ide, Hiroshi Mura...
CSB
2005
IEEE
165views Bioinformatics» more  CSB 2005»
9 years 5 months ago
Sequential Diagonal Linear Discriminant Analysis (SeqDLDA) for Microarray Classification and Gene Identification
In microarray classification we are faced with a very large number of features and very few training samples. This is a challenge for classical Linear Discriminant Analysis (LDA),...
Roger Pique-Regi, Antonio Ortega, Shahab Asgharzad...
GECCO
2006
Springer
156views Optimization» more  GECCO 2006»
9 years 7 months ago
A computational efficient covariance matrix update and a (1+1)-CMA for evolution strategies
First, the covariance matrix adaptation (CMA) with rankone update is introduced into the (1+1)-evolution strategy. An improved implementation of the 1/5-th success rule is propose...
Christian Igel, Thorsten Suttorp, Nikolaus Hansen
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