502 search results - page 71 / 101 » Principal Component Analysis for Sparse High-Dimensional Dat... |
ICCV
2007
IEEE
14 years 3 months ago
2007
IEEE
Dimensionality reduction plays a fundamental role in data processing, for which principal component analysis (PCA) is widely used. In this paper, we develop the Laplacian PCA (LPC...
ICPR
2008
IEEE
14 years 10 months ago
2008
IEEE
Speaker independent feature extraction is a critical problem in speech recognition. Oriented principal component analysis (OPCA) is a potential solution that can find a subspace r...
ICML
2005
IEEE
14 years 9 months ago
2005
IEEE
Recently, several manifold learning algorithms have been proposed, such as ISOMAP (Tenenbaum et al., 2000), Locally Linear Embedding (Roweis & Saul, 2000), Laplacian Eigenmap ...
ICANN
2003
Springer
14 years 1 months ago
2003
Springer
Abstract. An abstract recurrent neural network trained by an unsupervised method is applied to the kinematic control of a robot arm. The network is a novel extension of the Neural ...
IPM
2006
13 years 8 months ago
2006
A methodology for automatically identifying and clustering semantic features or topics in a heterogeneous text collection is presented. Textual data is encoded using a low rank no...