TY - GEN
T1 - Signal decomposition using multiscale admixture models
AU - Telgarsky, Matus
AU - Lafferty, John
PY - 2007
Y1 - 2007
N2 - Admixture models are "mixtures of mixtures" that decompose an object into multiple latent components, with the component proportions varying stochastically across objects. Recent work in machine learning has successfully developed admixture models for text, and work in population genetics has developed such models to analyze complex groups of individuals having mixed ancestry. We introduce a family of graphical admixture models for decomposing a signal into multiple components based on a wavelet representation of the signal. Two models are developed, one using a fixed segmentation of the signal, another using recursive dyadic partitioning. Variational algorithms are derived for inferring mixture proportions and estimating parameters.
AB - Admixture models are "mixtures of mixtures" that decompose an object into multiple latent components, with the component proportions varying stochastically across objects. Recent work in machine learning has successfully developed admixture models for text, and work in population genetics has developed such models to analyze complex groups of individuals having mixed ancestry. We introduce a family of graphical admixture models for decomposing a signal into multiple components based on a wavelet representation of the signal. Two models are developed, one using a fixed segmentation of the signal, another using recursive dyadic partitioning. Variational algorithms are derived for inferring mixture proportions and estimating parameters.
KW - Graphical model
KW - Recursive dyadic partitioning
KW - Unsupervised signal segmentation and labeling
KW - Variational inference
KW - Wavelets
UR - http://www.scopus.com/inward/record.url?scp=34547512940&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34547512940&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2007.366269
DO - 10.1109/ICASSP.2007.366269
M3 - Conference contribution
AN - SCOPUS:34547512940
SN - 1424407281
SN - 9781424407286
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - II449-II452
BT - 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
T2 - 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Y2 - 15 April 2007 through 20 April 2007
ER -