73 search results - page 4 / 15 » Sparse Unsupervised Dimensionality Reduction Algorithms |
BMCBI
2010
13 years 7 months ago
2010
Background: Visualization of DNA microarray data in two or three dimensional spaces is an important exploratory analysis step in order to detect quality issues or to generate new ...
ICONIP
2007
13 years 9 months ago
2007
Abstract. Principal component analysis (PCA) is a widely used technique for data analysis and dimensionality reduction. Eigenvalue decomposition is the standard algorithm for solvi...
JMM2
2008
13 years 7 months ago
2008
Unsupervised or Self-Organized learning algorithms have become very popular for discovery of significant patterns or features in the input data. The three prominent algorithms name...
PR
2010
13 years 6 months ago
2010
: Dimensionality reduction methods (DRs) have commonly been used as a principled way to understand the high-dimensional data such as face images. In this paper, we propose a new un...
ICML
2006
IEEE
14 years 8 months ago
2006
IEEE
We present a unified duality view of several recently emerged spectral methods for nonlinear dimensionality reduction, including Isomap, locally linear embedding, Laplacian eigenm...