71 search results - page 3 / 15 » Learning low dimensional predictive representations |
ICML
2005
IEEE
14 years 9 months ago
2005
IEEE
Dimensionality reduction is the problem of finding a low-dimensional representation of highdimensional input data. This paper examines the case where additional information is kno...
ICASSP
2011
IEEE
13 years 9 days ago
2011
IEEE
We present learning and inference algorithms for a versatile class of partially observed vector autoregressive (VAR) models for multivariate time-series data. VAR models can captu...
BMVC
2010
13 years 6 months ago
2010
We present a framework for the reduction of dimensionality of a data set via manifold learning. Using the building blocks of local hyperplanes we show how a global manifold can be...
WACV
2008
IEEE
14 years 3 months ago
2008
IEEE
Most face recognition algorithms use a “distancebased” approach: gallery and probe images are projected into a low dimensional feature space and decisions about matching are b...
ITCC
2005
IEEE
14 years 2 months ago
2005
IEEE
We propose a novel, computationally efficient generative topographic model for inferring low dimensional representations of high dimensional data sets, designed to exploit data s...