A map approach to learning sparse Gaussian Markov networks

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2009, Pages 1721-1724

  Bani Asadi, N ^a   Rish, I ^b   Scheinberg, K ^b   Kanevsky, D ^b   Ramabhadran, B ^b  

^a Stanford University   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

1 regularization; fMRI data analysis; Markov networks; Maximum a posteriory probability (MAP); Sparse optimization

Indexed keywords

BRAIN-IMAGING DATA; CONVEX PROBLEMS; EMPIRICAL RESULTS; FMRI DATA ANALYSIS; GAUSSIANS; MAP APPROACH; MARKOV NETWORKS; MAXIMUM A POSTERIORI PROBABILITIES; MAXIMUM A POSTERIORY PROBABILITY (MAP); OPEN PROBLEMS; OPTIMAL SELECTION; OPTIMIZATION METHOD; REAL-LIFE APPLICATIONS; REGULARIZATION PARAMETERS; SPARSE OPTIMIZATION; SYNTHETIC DATA;

ACOUSTICS; MAXIMUM LIKELIHOOD ESTIMATION; PROBABILITY; SIGNAL PROCESSING;

OPTIMIZATION;

EID: 70349207200     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2009.4959935     Document Type: Conference Paper

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