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» On the Optimality of the Dimensionality Reduction Method
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NIPS
2004
15 years 7 months ago
Proximity Graphs for Clustering and Manifold Learning
Many machine learning algorithms for clustering or dimensionality reduction take as input a cloud of points in Euclidean space, and construct a graph with the input data points as...
Miguel Á. Carreira-Perpiñán, ...
GECCO
2005
Springer
145views Optimization» more  GECCO 2005»
15 years 11 months ago
Three dimensional evolutionary aerodynamic design optimization with CMA-ES
In this paper, we present the application of evolutionary optimization methods to a demanding, industrially relevant engineering domain, the three-dimensional optimization of gas ...
Martina Hasenjäger, Bernhard Sendhoff, Toyota...
NIPS
2001
15 years 7 months ago
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motiva...
Mikhail Belkin, Partha Niyogi
IEEEMM
2007
146views more  IEEEMM 2007»
15 years 6 months ago
Learning Microarray Gene Expression Data by Hybrid Discriminant Analysis
— Microarray technology offers a high throughput means to study expression networks and gene regulatory networks in cells. The intrinsic nature of high dimensionality and small s...
Yijuan Lu, Qi Tian, Maribel Sanchez, Jennifer L. N...
CDC
2008
IEEE
16 years 17 days ago
Shannon meets Bellman: Feature based Markovian models for detection and optimization
— The goal of this paper is to develop modeling techniques for complex systems for the purposes of control, estimation, and inference: (i) A new class of Hidden Markov Models is ...
Sean P. Meyn, George Mathew