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ENTCS
2006
92views more  ENTCS 2006»
15 years 7 months ago
Nonstandard Meromorphic Groups
Extending the work of [7] on groups definable in compact complex manifolds and of [1] on strongly minimal groups definable in nonstandard compact complex manifolds, we classify al...
Thomas Scanlon
AAAI
2008
15 years 9 months ago
Manifold Integration with Markov Random Walks
Most manifold learning methods consider only one similarity matrix to induce a low-dimensional manifold embedded in data space. In practice, however, we often use multiple sensors...
Heeyoul Choi, Seungjin Choi, Yoonsuck Choe
198
Voted
COLT
2005
Springer
16 years 29 days ago
Towards a Theoretical Foundation for Laplacian-Based Manifold Methods
In recent years manifold methods have attracted a considerable amount of attention in machine learning. However most algorithms in that class may be termed ā€œmanifold-motivatedā€...
Mikhail Belkin, Partha Niyogi
226
Voted
ICASSP
2011
IEEE
14 years 11 months ago
Sampling on locally defined principal manifolds
We start with a locally defined principal curve definition for a given probability density function (pdf) and define a pairwise manifold score based on local derivatives of the...
Erhan Bas, Deniz Erdogmus
KDD
2007
ACM
276views Data Mining» more  KDD 2007»
16 years 7 months ago
Nonlinear adaptive distance metric learning for clustering
A good distance metric is crucial for many data mining tasks. To learn a metric in the unsupervised setting, most metric learning algorithms project observed data to a lowdimensio...
Jianhui Chen, Zheng Zhao, Jieping Ye, Huan Liu