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TNN
1998
111views more  TNN 1998»
13 years 7 months ago
Asymptotic distributions associated to Oja's learning equation for neural networks
— In this paper, we perform a complete asymptotic performance analysis of the stochastic approximation algorithm (denoted subspace network learning algorithm) derived from Oja’...
Jean Pierre Delmas, Jean-Francois Cardos
IEEEVAST
2010
13 years 2 months ago
Finding and visualizing relevant subspaces for clustering high-dimensional astronomical data using connected morphological opera
Data sets in astronomy are growing to enormous sizes. Modern astronomical surveys provide not only image data but also catalogues of millions of objects (stars, galaxies), each ob...
Bilkis J. Ferdosi, Hugo Buddelmeijer, Scott Trager...
ICML
2009
IEEE
14 years 2 months ago
Grammatical inference as a principal component analysis problem
One of the main problems in probabilistic grammatical inference consists in inferring a stochastic language, i.e. a probability distribution, in some class of probabilistic models...
Raphaël Bailly, François Denis, Liva R...
SIAMSC
2010
160views more  SIAMSC 2010»
13 years 6 months ago
Shift-Invert Arnoldi Approximation to the Toeplitz Matrix Exponential
The shift-invert Arnoldi method is employed to generate an orthonormal basis from the Krylov subspace corresponding to a real Toeplitz matrix and an initial vector. The vectors and...
Spike T. Lee, Hong-Kui Pang, Hai-Wei Sun
JMLR
2010
128views more  JMLR 2010»
13 years 2 months ago
Fluid Dynamics Models for Low Rank Discriminant Analysis
We consider the problem of reducing the dimensionality of labeled data for classification. Unfortunately, the optimal approach of finding the low-dimensional projection with minim...
Yung-Kyun Noh, Byoung-Tak Zhang, Daniel D. Lee