49 search results - page 5 / 10 » Dimensionality Reduction by Learning an Invariant Mapping |
AMDO
2006
Springer
13 years 11 months ago
2006
Springer
Abstract. We propose motion manifold learning and motion primitive segmentation framework for human motion synthesis from motion-captured data. High dimensional motion capture date...
IJON
2006
13 years 7 months ago
2006
In text management tasks, the dimensionality reduction becomes necessary to computation and interpretability of the results generated by machine learning algorithms. This paper de...
GRC
2010
IEEE
13 years 8 months ago
2010
IEEE
Inference of latent variables from complicated data is one important problem in data mining. The high dimensionality and high complexity of real world data often make accurate infe...
ICML
2008
IEEE
14 years 8 months ago
2008
IEEE
In this paper we introduce a novel approach to manifold alignment, based on Procrustes analysis. Our approach differs from "semisupervised alignment" in that it results ...
IJCAI
2007
13 years 9 months ago
2007
Dimensionality reduction is a much-studied task in machine learning in which high-dimensional data is mapped, possibly via a non-linear transformation, onto a low-dimensional mani...