PR
2011
13 years 2 months ago
2011
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...
AMC
2011
13 years 3 months ago
2011
：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...
ICASSP
2011
IEEE
13 years 3 months ago
2011
IEEE
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...
TSP
2010
13 years 6 months ago
2010
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...
JMLR
2010
13 years 6 months ago
2010
We introduce a variational inference framework for training the Gaussian process latent variable model and thus performing Bayesian nonlinear dimensionality reduction. This method...
TSP
2011
13 years 6 months ago
2011
—This work proposes a blind adaptive reduced-rank scheme and constrained constant-modulus (CCM) adaptive algorithms for interference suppression in wireless communications system...
PAMI
2011
13 years 6 months ago
2011
—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 ...
CORR
2011
Springer
13 years 6 months ago
2011
Springer
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...
AAAI
2010
13 years 8 months ago
2010
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...
COMSIS
2010
13 years 9 months ago
2010
Dimensionality reduction is an important preprocessing step in high-dimensional data analysis without losing intrinsic information. The problem of semi-supervised nonlinear dimensi...