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PR
2006
147views more  PR 2006»
13 years 8 months ago
Robust locally linear embedding
In the past few years, some nonlinear dimensionality reduction (NLDR) or nonlinear manifold learning methods have aroused a great deal of interest in the machine learning communit...
Hong Chang, Dit-Yan Yeung
NN
2008
Springer
101views Neural Networks» more  NN 2008»
13 years 8 months ago
On multidimensional scaling and the embedding of self-organising maps
The self-organising map (SOM) and its variant, visualisation induced SOM (ViSOM), have been known to yield similar results to multidimensional scaling (MDS). However, the exact co...
Hujun Yin
ACCV
2007
Springer
14 years 3 months ago
Analyzing Facial Expression by Fusing Manifolds
Feature representation and classification are two major issues in facial expression analysis. In the past, most methods used either holistic or local representation for analysis. ...
Wen-Yan Chang, Chu-Song Chen, Yi-Ping Hung
STOC
2006
ACM
244views Algorithms» more  STOC 2006»
14 years 9 months ago
Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform
We introduce a new low-distortion embedding of d 2 into O(log n) p (p = 1, 2), called the Fast-Johnson-LindenstraussTransform. The FJLT is faster than standard random projections ...
Nir Ailon, Bernard Chazelle
PR
2006
108views more  PR 2006»
13 years 8 months ago
Boosted discriminant projections for nearest neighbor classification
In this paper we introduce a new embedding technique to find the linear projection that best projects labeled data samples into a new space where the performance of a Nearest Neig...
David Masip, Jordi Vitrià