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» Semi-Supervised Dimensionality Reduction
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AAAI
2008
15 years 4 months ago
Transfer Learning via Dimensionality Reduction
Transfer learning addresses the problem of how to utilize plenty of labeled data in a source domain to solve related but different problems in a target domain, even when the train...
Sinno Jialin Pan, James T. Kwok, Qiang Yang
ACAL
2007
Springer
15 years 3 months ago
Modelling Architectural Visual Experience Using Non-linear Dimensionality Reduction
This paper addresses the topic of how architectural visual experience can be represented and utilised by a software system. The long-term aim is to equip an artificial agent with ...
Stephan K. Chalup, Riley Clement, Chris Tucker, Mi...
IJCAI
2003
15 years 3 months ago
Continuous nonlinear dimensionality reduction by kernel Eigenmaps
We equate nonlinear dimensionality reduction (NLDR) to graph embedding with side information about the vertices, and derive a solution to either problem in the form of a kernel-ba...
Matthew Brand
SODA
2000
ACM
127views Algorithms» more  SODA 2000»
15 years 3 months ago
Dimensionality reduction techniques for proximity problems
In this paper we give approximation algorithms for several proximity problems in high dimensional spaces. In particular, we give the rst Las Vegas data structure for (1 + )-neares...
Piotr Indyk
PAMI
2006
141views more  PAMI 2006»
15 years 1 months ago
Diffusion Maps and Coarse-Graining: A Unified Framework for Dimensionality Reduction, Graph Partitioning, and Data Set Parameter
We provide evidence that non-linear dimensionality reduction, clustering and data set parameterization can be solved within one and the same framework. The main idea is to define ...
Stéphane Lafon, Ann B. Lee