317 search results - page 8 / 64 » From Transformation-Based Dimensionality Reduction to Featur... |
104
Voted
SAC
2006
ACM
15 years 3 months ago
2006
ACM
“The curse of dimensionality” is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and classification error in high dimension...
ICML
2005
IEEE
15 years 10 months ago
2005
IEEE
Many unsupervised algorithms for nonlinear dimensionality reduction, such as locally linear embedding (LLE) and Laplacian eigenmaps, are derived from the spectral decompositions o...
ECCV
2004
Springer
15 years 11 months ago
2004
Springer
A linear, discriminative, supervised technique for reducing feature vectors extracted from image data to a lower-dimensional representation is proposed. It is derived from classica...
SDM
2008
SIAM
14 years 11 months ago
2008
SIAM
In this paper we propose and test the use of hierarchical clustering for feature selection. The clustering method is Ward's with a distance measure based on GoodmanKruskal ta...
NN
2010
Springer
14 years 7 months ago
2010
Springer
The ratio of two probability density functions is becoming a quantity of interest these days in the machine learning and data mining communities since it can be used for various d...