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» Dimensionality reduction techniques for proximity problems
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PR
2006
147views more  PR 2006»
13 years 7 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
CIKM
2008
Springer
13 years 9 months ago
On low dimensional random projections and similarity search
Random projection (RP) is a common technique for dimensionality reduction under L2 norm for which many significant space embedding results have been demonstrated. However, many si...
Yu-En Lu, Pietro Liò, Steven Hand
ECCV
2004
Springer
14 years 9 months ago
Many-to-Many Feature Matching Using Spherical Coding of Directed Graphs
In recent work, we presented a framework for many-to-many matching of multi-scale feature hierarchies, in which features and their relations were captured in a vertex-labeled, edge...
M. Fatih Demirci, Ali Shokoufandeh, Sven J. Dickin...
ICML
2006
IEEE
14 years 8 months ago
Discriminative cluster analysis
Clustering is one of the most widely used statistical tools for data analysis. Among all existing clustering techniques, k-means is a very popular method because of its ease of pr...
Fernando De la Torre, Takeo Kanade
CACM
2010
104views more  CACM 2010»
13 years 7 months ago
Faster dimension reduction
Data represented geometrically in high-dimensional vector spaces can be found in many applications. Images and videos, are often represented by assigning a dimension for every pix...
Nir Ailon, Bernard Chazelle