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ICML
2010
IEEE
13 years 6 months ago
Local Minima Embedding
Dimensionality reduction is a commonly used step in many algorithms for visualization, classification, clustering and modeling. Most dimensionality reduction algorithms find a low...
Minyoung Kim, Fernando De la Torre
PR
2006
147views more  PR 2006»
13 years 5 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
ESANN
2003
13 years 7 months ago
Locally Linear Embedding versus Isotop
Abstract. Recently, a new method intended to realize conformal mappings has been published. Called Locally Linear Embedding (LLE), this method can map high-dimensional data lying o...
John Aldo Lee, Cédric Archambeau, Michel Ve...
CORR
2006
Springer
125views Education» more  CORR 2006»
13 years 5 months ago
Barriers and local minima in energy landscapes of stochastic local search
: A local search algorithm operating on an instance of a Boolean constraint satisfaction problem (in particular, k-SAT) can be viewed as a stochastic process traversing successive ...
Petteri Kaski
JCO
2007
123views more  JCO 2007»
13 years 5 months ago
On the number of local minima for the multidimensional assignment problem
The Multidimensional Assignment Problem (MAP) is an NP-hard combinatorial optimization problem occurring in many applications, such as data association, target tracking, and resou...
Don A. Grundel, Pavlo A. Krokhmal, Carlos A. S. Ol...