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PR
2011
9 years 1 months ago
A survey of multilinear subspace learning for tensor data
Increasingly large amount of multidimensional data are being generated on a daily basis in many applications. This leads to a strong demand for learning algorithms to extract usef...
Haiping Lu, Konstantinos N. Plataniotis, Anastasio...
AMC
2011
9 years 2 months ago
Large correlation analysis
:In this paper, a novel supervised dimensionality reduction method is developed based on both the correlation analysis and the idea of large margin learning. The method aims to m...
Xiaohong Chen, Songcan Chen, Hui Xue
ICASSP
2011
IEEE
9 years 2 months ago
Compressed classification of observation sets with linear subspace embeddings
We consider the problem of classification of a pattern from multiple compressed observations that are collected in a sensor network. In particular, we exploit the properties of r...
Dorina Thanou, Pascal Frossard
TSP
2010
9 years 5 months ago
Optimal/near-optimal dimensionality reduction for distributed estimation in homogeneous and certain inhomogeneous scenarios
We consider distributed estimation of a deterministic vector parameter from noisy sensor observations in a wireless sensor network (WSN). The observation noise is assumed uncorrela...
Jun Fang, Hongbin Li
JMLR
2010
155views more  JMLR 2010»
9 years 5 months ago
Bayesian Gaussian Process Latent Variable Model
We introduce a variational inference framework for training the Gaussian process latent variable model and thus performing Bayesian nonlinear dimensionality reduction. This method...
Michalis Titsias, Neil D. Lawrence
TSP
2011
152views more  TSP 2011»
9 years 5 months ago
Blind Adaptive Constrained Constant-Modulus Reduced-Rank Interference Suppression Algorithms Based on Interpolation and Switched
—This work proposes a blind adaptive reduced-rank scheme and constrained constant-modulus (CCM) adaptive algorithms for interference suppression in wireless communications system...
Rodrigo C. de Lamare, Raimundo Sampaio Neto, Marti...
PAMI
2011
9 years 5 months ago
Multiple Kernel Learning for Dimensionality Reduction
—In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. The resulting ...
Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh
CORR
2011
Springer
151views Education» more  CORR 2011»
9 years 5 months ago
A supervised clustering approach for fMRI-based inference of brain states
We propose a method that combines signals from many brain regions observed in functional Magnetic Resonance Imaging (fMRI) to predict the subject’s behavior during a scanning se...
Vincent Michel, Alexandre Gramfort, Gaël Varo...
AAAI
2010
9 years 7 months ago
Multilinear Maximum Distance Embedding Via L1-Norm Optimization
Dimensionality reduction plays an important role in many machine learning and pattern recognition tasks. In this paper, we present a novel dimensionality reduction algorithm calle...
Yang Liu, Yan Liu, Keith C. C. Chan
COMSIS
2010
9 years 8 months ago
Effective semi-supervised nonlinear dimensionality reduction for wood defects recognition
Dimensionality reduction is an important preprocessing step in high-dimensional data analysis without losing intrinsic information. The problem of semi-supervised nonlinear dimensi...
Zhao Zhang, Ning Ye
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