60 search results - page 10 / 12 » Continuous nonlinear dimensionality reduction by kernel Eige... |
ICML
2004
IEEE
14 years 6 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...
CVPR
2007
IEEE
14 years 7 months ago
2007
IEEE
We describe a probabilistic framework for recognizing human activities in monocular video based on simple silhouette observations in this paper. The methodology combines kernel pr...
PR
2007
13 years 5 months ago
2007
Isomap is one of widely-used low-dimensional embedding methods, where geodesic distances on a weighted graph are incorporated with the classical scaling (metric multidimensional s...
ICCV
2007
IEEE
14 years 3 days 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...
BIRD
2007
Springer
13 years 12 months ago
2007
Springer
Experimental time courses often reveal a nonlinear behaviour. Analysing these nonlinearities is even more challenging when the observed phenomenon is cyclic or oscillatory. This me...