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» Nonlinear principal component analysis of noisy data
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ICML
2007
IEEE
14 years 8 months ago
Nonlinear independent component analysis with minimal nonlinear distortion
Nonlinear ICA may not result in nonlinear blind source separation, since solutions to nonlinear ICA are highly non-unique. In practice, the nonlinearity in the data generation pro...
Kun Zhang, Laiwan Chan
NIPS
1998
13 years 9 months ago
Learning a Continuous Hidden Variable Model for Binary Data
A directed generative model for binary data using a small number of hidden continuous units is investigated. A clipping nonlinearity distinguishes the model from conventional prin...
Daniel D. Lee, Haim Sompolinsky
CIKM
2010
Springer
13 years 6 months ago
Decomposing background topics from keywords by principal component pursuit
Low-dimensional topic models have been proven very useful for modeling a large corpus of documents that share a relatively small number of topics. Dimensionality reduction tools s...
Kerui Min, Zhengdong Zhang, John Wright, Yi Ma
ICPR
2006
IEEE
14 years 8 months ago
Dimensionality Reduction with Adaptive Kernels
1 A kernel determines the inductive bias of a learning algorithm on a specific data set, and it is beneficial to design specific kernel for a given data set. In this work, we propo...
Shuicheng Yan, Xiaoou Tang
KDD
2001
ACM
187views Data Mining» more  KDD 2001»
14 years 8 months ago
Random projection in dimensionality reduction: applications to image and text data
Random projections have recently emerged as a powerful method for dimensionality reduction. Theoretical results indicate that the method preserves distances quite nicely; however,...
Ella Bingham, Heikki Mannila