TY - GEN
T1 - Covariate-dependent dictionary learning and sparse coding
AU - Zhou, Mingyuan
AU - Yang, Hongxia
AU - Sapiro, Guillermo
AU - Dunson, David
AU - Carin, Lawrence
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09
PY - 2011/8/18
Y1 - 2011/8/18
N2 - A dependent hierarchical beta process (dHBP) is developed as a prior for data that may be represented in terms of a sparse set of latent features (dictionary elements), with covariate-dependent feature usage. The dHBP is applicable to general covariates and data models, imposing that signals with similar covariates are likely to be manifested in terms of similar features. As an application, we consider the simultaneous sparse modeling of multiple images, with the covariate of a given image linked to its similarity to all other images (as applied in manifold learning). Efficient inference is performed using hybrid Gibbs, Metropolis-Hastings and slice sampling. © 2011 IEEE.
AB - A dependent hierarchical beta process (dHBP) is developed as a prior for data that may be represented in terms of a sparse set of latent features (dictionary elements), with covariate-dependent feature usage. The dHBP is applicable to general covariates and data models, imposing that signals with similar covariates are likely to be manifested in terms of similar features. As an application, we consider the simultaneous sparse modeling of multiple images, with the covariate of a given image linked to its similarity to all other images (as applied in manifold learning). Efficient inference is performed using hybrid Gibbs, Metropolis-Hastings and slice sampling. © 2011 IEEE.
UR - http://ieeexplore.ieee.org/document/5947685/
UR - http://www.scopus.com/inward/record.url?scp=80051602594&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2011.5947685
DO - 10.1109/ICASSP.2011.5947685
M3 - Conference contribution
SN - 9781457705397
SP - 5824
EP - 5827
BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ER -