49 search results - page 6 / 10 » Dimensionality Reduction by Learning an Invariant Mapping |
ACMACE
2008
ACM
13 years 9 months ago
2008
ACM
Dimensionality reduction is a statistical tool commonly used to map high-dimensional data into lower a dimensionality. The transformed data is typically more suitable for regressi...
ICML
2004
IEEE
14 years 8 months ago
2004
IEEE
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...
FGR
2006
IEEE
14 years 1 months ago
2006
IEEE
We propose an Isometric Self-Organizing Map (ISOSOM) method for nonlinear dimensionality reduction, which integrates a Self-Organizing Map model and an ISOMAP dimension reduction ...
AAAI
2010
13 years 4 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...
ICCV
2007
IEEE
14 years 1 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...