PR
2011
12 years 7 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
12 years 8 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
12 years 8 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
12 years 11 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
12 years 11 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
12 years 11 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
12 years 11 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
12 years 11 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 1 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 2 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...