Conference Paper
Indian Buffet Processes with Powerlaw Behavior.
Conference: Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 710 December 2009, Vancouver, British Columbia, Canada.
Source: DBLP

Article: Learning LowDimensional Signal Models: A Bayesian approach based on incomplete measurements.
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ABSTRACT: Sampling, coding, and streaming even the most essential data, e.g., in medical imaging and weathermonitoring applications, produce a data deluge that severely stresses the avail able analogtodigital converter, communication bandwidth, and digitalstorage resources. Surprisingly, while the ambient data dimension is large in many problems, the relevant information in the data can reside in a much lower dimensional space. This observation has led to several important theoretical and algorithmic developments under different lowdimensional modeling frameworks, such as compressive sensing (CS), matrix completion, and general factormodel representations. These approaches have enabled new measurement systems, tools, and methods for information extraction from dimensionalityreduced or incomplete data. A key aspect of maximizing the potential of such techniques is to develop appropriate data models. In this article, we investigate this challenge from the perspective of nonparametric Bayesian analysis.IEEE Signal Processing Magazine 03/2011; 28(2). · 3.37 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: Modelling is fundamental to many fields of science and engineering. A model can be thought of as a representation of possible data one could predict from a system. The probabilistic approach to modelling uses probability theory to express all aspects of uncertainty in the model. The probabilistic approach is synonymous with Bayesian modelling, which simply uses the rules of probability theory in order to make predictions, compare alternative models, and learn model parameters and structure from data. This simple and elegant framework is most powerful when coupled with flexible probabilistic models. Flexibility is achieved through the use of Bayesian nonparametrics. This article provides an overview of probabilistic modelling and an accessible survey of some of the main tools in Bayesian nonparametrics. The survey covers the use of Bayesian nonparametrics for modelling unknown functions, density estimation, clustering, timeseries modelling, and representing sparsity, hierarchies, and covariance structure. More specifically, it gives brief nontechnical overviews of Gaussian processes, Dirichlet processes, infinite hidden Markov models, Indian buffet processes, Kingman's coalescent, Dirichlet diffusion trees and Wishart processes.Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences 01/2013; 371(1984):20110553. · 2.89 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: The threeparameter Indian buffet process is generalized. The possibly different role played by customers is taken into account by suitable (random) weights. Various limit theorems are also proved for such generalized Indian buffet process. Let L_n be the number of dishes experimented by the first n customers, and let {\bar K}_n = (1/n)\sum_{i=1}^n K_i where K_i is the number of dishes tried by customer i. The asymptotic distributions of L_n and {\bar K}_n, suitably centered and scaled, are obtained. The convergence turns out to be stable (and not only in distribution). As a particular case, the results apply to the standard (i.e., not generalized) Indian buffet process.04/2013;
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