49 search results - page 6 / 10 » Dimensionality Reduction by Learning an Invariant Mapping |
127
Voted
ACMACE
2008
ACM
15 years 5 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...
206
Voted
ICML
2004
IEEE
16 years 4 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...
126
Voted
FGR
2006
IEEE
15 years 9 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 ...
152
Voted
AAAI
2010
15 years 14 days 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...
128
Voted
ICCV
2007
IEEE
15 years 10 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...