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» Linear State-Space Models for Blind Source Separation
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IJON
1998
87views more  IJON 1998»
13 years 5 months ago
Learned parametric mixture based ICA algorithm
The learned parametric mixture method is presented for a canonical cost function based ICA model on linear mixture, with several new findings. First, its adaptive algorithm is fu...
Lei Xu, Chi Chiu Cheung, Shun-ichi Amari
CORR
2010
Springer
207views Education» more  CORR 2010»
13 years 5 months ago
Collaborative Hierarchical Sparse Modeling
Sparse modeling is a powerful framework for data analysis and processing. Traditionally, encoding in this framework is performed by solving an 1-regularized linear regression prob...
Pablo Sprechmann, Ignacio Ramírez, Guillerm...
ICASSP
2011
IEEE
12 years 9 months ago
Cosparse analysis modeling - uniqueness and algorithms
In the past decade there has been a great interest in a synthesis-based model for signals, based on sparse and redundant representations. Such a model assumes that the signal of i...
Sangnam Nam, Michael E. Davies, Michael Elad, R&ea...
EDM
2010
160views Data Mining» more  EDM 2010»
13 years 7 months ago
Using Neural Imaging and Cognitive Modeling to Infer Mental States while Using an Intelligent Tutoring System
Functional magnetic resonance imaging (fMRI) data were collected while students worked with a tutoring system that taught an algebra isomorph. A cognitive model predicted the distr...
Jon M. Fincham, John R. Anderson, Shawn Betts, Jen...
ICANNGA
2007
Springer
191views Algorithms» more  ICANNGA 2007»
13 years 11 months ago
Novel Multi-layer Non-negative Tensor Factorization with Sparsity Constraints
In this paper we present a new method of 3D non-negative tensor factorization (NTF) that is robust in the presence of noise and has many potential applications, including multi-way...
Andrzej Cichocki, Rafal Zdunek, Seungjin Choi, Rob...