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ICML
2004
IEEE
16 years 7 days ago
Learning a kernel matrix for nonlinear dimensionality reduction
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into ...
Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul
GMP
2006
IEEE
151views Solid Modeling» more  GMP 2006»
16 years 26 days ago
A Surface Displaced from a Manifold
We present a new surface representation scheme based on a manifold structure and displacement functions. Given a geometric model represented as a point cloud, we construct a domain...
Seung-Hyun Yoon
NIPS
2008
15 years 8 months ago
Convergence and Rate of Convergence of a Manifold-Based Dimension Reduction Algorithm
We study the convergence and the rate of convergence of a local manifold learning algorithm: LTSA [13]. The main technical tool is the perturbation analysis on the linear invarian...
Andrew Smith, Xiaoming Huo, Hongyuan Zha
RECOMB
2002
Springer
16 years 7 months ago
Discovering local structure in gene expression data: the order-preserving submatrix problem
This paper concerns the discovery of patterns in gene expression matrices, in which each element gives the expression level of a given gene in a given experiment. Most existing me...
Amir Ben-Dor, Benny Chor, Richard M. Karp, Zohar Y...
ECTEL
2008
Springer
15 years 8 months ago
A Goal-oriented Authoring Approach to Design, Share and Reuse Learning Scenarios
This paper presents our research works and our proposal : ISiS model (Intentions, Strategies, interactional Situations), a conceptual framework elaborated to structure the design o...
Valérie Emin