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The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features
[article]
2012
arXiv
pre-print
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Drawing on ideas from graphical models and phylogenetics, we describe a non-exchangeable prior for a class of nonparametric latent feature models that is nearly as efficient computationally as its exchangeable ...
., bag-of-words models for documents), exchangeability is sometimes assumed simply for computational reasons; non-exchangeable models might be a better choice for applications based on subject matter. ...
Acknowledgements This work was supported by the UC Berkeley Chancellor's Faculty Partnership Fund, by grant BCS-0631518 from the National Science Foundation and by a grant from the Lawrence Livermore National ...
arXiv:1206.3279v1
fatcat:thvpqcx4qzfztgs76frize3uny
Posterior Contraction Rates of the Phylogenetic Indian Buffet Processes
2016
Bayesian Analysis
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The phylogenetic Indian buffet process (pIBP), a derivative of IBP, enables the modeling of non-exchangeability among subjects through a stochastic process on a rooted tree, which is similar to that used ...
The Indian buffet process (IBP) is such an example that can be used to define a prior distribution on infinite binary features, where the exchangeability among subjects is assumed. ...
Phylogenetic Indian Buffet Process The phylogenetic Indian buffet process (pIBP) also starts with drawing {p k } as in (2) . ...
doi:10.1214/15-ba958
pmid:27087886
pmcid:PMC4830498
fatcat:rwfv3obzkveehl5oihmo3y52v4
Posterior Contraction Rates of the Phylogenetic Indian Buffet Processes
[article]
2015
arXiv
pre-print
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The phylogenetic Indian buffet process (pIBP), a derivative of IBP, enables the modeling of non-exchangeability among subjects through a stochastic process on a rooted tree, which is similar to that used ...
The Indian buffet process (IBP) is such an example that can be used to define a prior distribution on infinite binary features, where the exchangeability among subjects is assumed. ...
Phylogenetic Indian Buffet Process The phylogenetic Indian buffet process (pIBP) also starts with drawing {p k } as in (2) . ...
arXiv:1307.8229v2
fatcat:iutgfrctsjdrzg4dtmpbb45lti
The Indian Buffet Process: An Introduction and Review
2011
Journal of machine learning research
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We give a detailed derivation of this distribution, and illustrate its use as a prior in an infinite latent feature model. ...
The Indian buffet process is a stochastic process defining a probability distribution over equivalence classes of sparse binary matrices with a finite number of rows and an unbounded number of columns. ...
We thank three anonymous reviewers for their comments on the manuscript. ...
dblp:journals/jmlr/GriffithsG11
fatcat:6q354ce3snd6pojgol4skovy6y
Dependent Indian Buffet Process-based Sparse Nonparametric Nonnegative Matrix Factorization
[article]
2015
arXiv
pre-print
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In this paper, we propose a nonparametric NMF framework to mitigate this issue by using dependent Indian Buffet Processes (dIBP). ...
However, traditional NMF methods typically assume the number of latent factors (i.e., dimensionality of the loading matrices) to be fixed. This assumption makes them inflexible for many applications. ...
Jordan, “The phylogenetic in-
negative matrix-factorization-based approach to collaborative dian buffet process: A non-exchangeable nonparametric prior
filtering for recommender systems ...
arXiv:1507.03176v1
fatcat:jdt3mihx5fftng3n4nb2hympdq
Inferring Interaction Networks using the IBP applied to microRNA Target Prediction
2011
Neural Information Processing Systems
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Here we extend the Indian Buffet Process (IBP), a nonparametric Bayesian model, to integrate noisy interaction scores with properties of individual entities for inferring interaction networks and clustering ...
Analysis of synthetic and real data indicates that the method improves upon prior methods, correctly recovers interactions and clusters, and provides accurate biological predictions. ...
In [11] , Miller et al. presented the phylogenetic Indian Buffet Process (pIBP), where they used a tree representation to express non-exchangeability. ...
dblp:conf/nips/LeB11
fatcat:wlkzjxanonecbgvxyntpxqelhy
Distance Dependent Infinite Latent Feature Models
2015
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We present a generalization of the IBP, the distance dependent Indian buffet process (dd-IBP), for modeling non-exchangeable data. ...
Bayesian nonparametric variants of these models, which use the Indian buffet process (IBP) as a prior over latent features, allow the number of features to be determined from the data. ...
Other non-exchangeable variants Several other non-exchangeable priors for infinite latent feature models have been proposed (see [14] for a comprehensive review). ...
doi:10.1109/tpami.2014.2321387
pmid:26353245
fatcat:nll2c67msjbp5izytyfuwctqnq
Dependent Hierarchical Beta Process for Image Interpolation and Denoising
2011
Journal of machine learning research
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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, with covariate-dependent feature usage. ...
Coupling the dHBP with the Bernoulli process, and upon marginalizing out the dHBP, the model may be interpreted as a covariatedependent hierarchical Indian buffet process. ...
Acknowledgements The research reported here was supported by AFOSR, ARO, DARPA, DOE, NGA, ONR and SERDP. ...
dblp:journals/jmlr/ZhouYSDC11
fatcat:qdrtoeydzbddrcfidfehfcdlfy
The Kernel Beta Process
2011
Neural Information Processing Systems
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A new Lévy process prior is proposed for an uncountable collection of covariatedependent feature-learning measures; the model is called the kernel beta process (KBP). ...
Each customer selects dishes from an infinite buffet, in a manner analogous to the beta process, with the added constraint that a customer first decides probabilistically whether to "consider" a dish, ...
Acknowledgment The research reported here was supported by AFOSR, ARO, DARPA, DOE, NGA and ONR. ...
dblp:conf/nips/RenWDC11
fatcat:z3epvrm46zhgtc6s46ian2mezi
Distance Dependent Infinite Latent Feature Models
[article]
2012
arXiv
pre-print
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Bayesian nonparametric variants of these models, which use the Indian buffet process (IBP) as a prior over latent features, allow the number of features to be determined from the data. ...
We present a generalization of the IBP, the distance dependent Indian buffet process (dd-IBP), for modeling non-exchangeable data. ...
Acknowledgements SJG was supported by a NSF graduate research fellowship. PIF acknowledges support from NSF Award #142251. ...
arXiv:1110.5454v2
fatcat:ql7jfxvvxndhxawkcyxkhf6fqu
Correlated Non-Parametric Latent Feature Models
[article]
2012
arXiv
pre-print
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When the number of hidden features is unknown, the Indian Buffet Process (IBP) is a nonparametric latent feature model that does not bound the number of active features in dataset. ...
We introduce a framework for correlated nonparametric feature models, generalising the IBP. ...
Acknowledgements FD was supported by a Marshall scholarship. ...
arXiv:1205.2650v1
fatcat:innmjtrgzbd3tfczhqsftdebci
Bayesian Nonparametric Models for Biomedical Data Analysis
[article]
2017
arXiv
pre-print
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In contrast to commonly used feature allocation models, we allow the latent features to be dependent, using a tree structure to introduce dependence. ...
In the same context of using mutation pairs, in order to recover the phylogenetic relationship of subclones, we then develop a Bayesian treed feature allocation model. ...
For example, Figure 1 .
Indian Buffet Process The Indian buffet process (IBP) Ghahramani, 2006, 2011)) is a popular example of an exchangeable random feature allocation. ...
arXiv:1710.09890v1
fatcat:kvjysjkjirgt5dvmusedegibdq
Probabilistic Non-Negative Matrix Factorization with Binary Components
2021
Mathematics
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In order to automatically learn the potential binary features and feature number, a deterministic Indian buffet process variational inference is introduced to obtain the binary factor matrix. ...
It has proven to be a powerful low-rank decomposition technique for non-negative multivariate data. However, its performance largely depends on the assumption of a fixed number of features. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/math9111189
fatcat:bll4zcwnmfbv5h23xlyw6kyxwa
BAYCLONE: BAYESIAN NONPARAMETRIC INFERENCE OF TUMOR SUBCLONES USING NGS DATA
2014
Biocomputing 2015
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Taking a Bayesian approach, we extend the Indian buffet process (IBP) to define a class of nonparametric models, the categorical IBP (cIBP). ...
Instead of partitioning somatic mutations into non-overlapping clusters with similar cellular prevalences, we took a different approach using feature allocation. ...
Briefly, we could consider a sampling model for the total read counts N st to estimate the sample copy numbers, conditional on which a couple of feature allocation models can be linked for estimating subclonal ...
doi:10.1142/9789814644730_0044
fatcat:ol6jan4wkfdovftbttykifrrze
Bayesian biclustering for microbial metagenomic sequencing data via multinomial matrix factorization
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2005.08361v1 fatcat:sq5piakuyrgihfjpxhwcthum3m
Our findings can help generate potential hypotheses for future investigation of the heterogeneity of the human gut microbiome. ...
The proposed method represents the observed over-dispersed zero-inflated count matrix as Dirichlet-multinomial mixtures on which latent cluster structures are built hierarchically. ...
Specifically, we construct the hierarchical model with a combination of latent logit model, phylogenetic Indian buffet process prior (pIBP, Miller and others, 2008; Chen and others, 2016) , and beta-Bernoulli ...
arXiv:2005.08361v2
fatcat:sbwmtxlr7zclpcrj7cbhlzhpvq
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