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TSP
2010
13 years 2 months ago
Gaussian multiresolution models: exploiting sparse Markov and covariance structure
We consider the problem of learning Gaussian multiresolution (MR) models in which data are only available at the finest scale and the coarser, hidden variables serve both to captu...
Myung Jin Choi, Venkat Chandrasekaran, Alan S. Wil...
IEICET
2008
95views more  IEICET 2008»
13 years 7 months ago
Approximating the Best Linear Unbiased Estimator of Non-Gaussian Signals with Gaussian Noise
Obtaining the best linear unbiased estimator (BLUE) of noisy signals is a traditional but powerful approach to noise reduction. Explicitly computing the BLUE usually requires the ...
Masashi Sugiyama, Motoaki Kawanabe, Gilles Blancha...
SDM
2009
SIAM
202views Data Mining» more  SDM 2009»
14 years 4 months ago
Proximity-Based Anomaly Detection Using Sparse Structure Learning.
We consider the task of performing anomaly detection in highly noisy multivariate data. In many applications involving real-valued time-series data, such as physical sensor data a...
Tsuyoshi Idé, Aurelie C. Lozano, Naoki Abe,...
ICPR
2010
IEEE
13 years 5 months ago
Information Theoretic Expectation Maximization Based Gaussian Mixture Modeling for Speaker Verification
The expectation maximization (EM) algorithm is widely used in the Gaussian mixture model (GMM) as the state-of-art statistical modeling technique. Like the classical EM method, th...
Sheeraz Memon, Margaret Lech, Namunu Chinthaka Mad...
ICML
2008
IEEE
14 years 8 months ago
Gaussian process product models for nonparametric nonstationarity
Stationarity is often an unrealistic prior assumption for Gaussian process regression. One solution is to predefine an explicit nonstationary covariance function, but such covaria...
Ryan Prescott Adams, Oliver Stegle