Results 1  10
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55
A tutorial on Bayesian nonparametric models
 Journal of Mathematical Psychology
"... A key problem in statistical modeling is model selection, how to choose a model at an appropriate level of complexity. This problem appears in many settings, most prominently in choosing the number of clusters in mixture models or the number of factors in factor analysis. In this tutorial we describ ..."
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Cited by 39 (8 self)
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A key problem in statistical modeling is model selection, how to choose a model at an appropriate level of complexity. This problem appears in many settings, most prominently in choosing the number of clusters in mixture models or the number of factors in factor analysis. In this tutorial we describe Bayesian nonparametric methods, a class of methods that sidesteps this issue by allowing the data to determine the complexity of the model. This tutorial is a highlevel introduction to Bayesian nonparametric methods and contains several examples of their application. 1
Evolutionary hierarchical Dirichlet processes for multiple correlated timevarying corpora
 In KDD
, 2010
"... Mining cluster evolution from multiple correlated timevarying text corpora is important in exploratory text analytics. In this paper, we propose an approach called evolutionary hierarchical Dirichlet processes (EvoHDP) to discover interesting cluster evolution patterns from such text data. We formu ..."
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Cited by 29 (6 self)
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Mining cluster evolution from multiple correlated timevarying text corpora is important in exploratory text analytics. In this paper, we propose an approach called evolutionary hierarchical Dirichlet processes (EvoHDP) to discover interesting cluster evolution patterns from such text data. We formulate the EvoHDP as a series of hierarchical Dirichlet processes (HDP) by adding time dependencies to the adjacent epochs, and propose a cascaded Gibbs sampling scheme to infer the model. This approach can discover different evolving patterns of clusters, including emergence, disappearance, evolution within a corpus and across different corpora. Experiments over synthetic and realworld multiple correlated timevarying data sets illustrate the effectiveness of EvoHDP on discovering cluster evolution patterns.
A Bayesian Approach to Unsupervised Semantic Role Induction
"... We introduce two Bayesian models for unsupervised semantic role labeling (SRL) task. The models treat SRL as clustering of syntactic signatures of arguments with clusters corresponding to semantic roles. The first model induces these clusterings independently for each predicate, exploiting the Chine ..."
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Cited by 15 (4 self)
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We introduce two Bayesian models for unsupervised semantic role labeling (SRL) task. The models treat SRL as clustering of syntactic signatures of arguments with clusters corresponding to semantic roles. The first model induces these clusterings independently for each predicate, exploiting the Chinese Restaurant Process (CRP) as a prior. In a more refined hierarchical model, we inject the intuition that the clusterings are similar across different predicates, even though they are not necessarily identical. This intuition is encoded as a distancedependent CRP with a distance between two syntactic signatures indicating how likely they are to correspond to a single semantic role. These distances are automatically induced within the model and shared across predicates. Both models achieve stateoftheart results when evaluated on PropBank, with the coupled model consistently outperforming the factored counterpart in all experimental setups. 1
Spatial distance dependent Chinese restaurant processes for image segmentation
"... The distance dependent Chinese restaurant process (ddCRP) was recently introduced to accommodate random partitions of nonexchangeable data [1]. The ddCRP clusters data in a biased way: each data point is more likely to be clustered with other data that are near it in an external sense. This paper e ..."
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Cited by 12 (4 self)
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The distance dependent Chinese restaurant process (ddCRP) was recently introduced to accommodate random partitions of nonexchangeable data [1]. The ddCRP clusters data in a biased way: each data point is more likely to be clustered with other data that are near it in an external sense. This paper examines the ddCRP in a spatial setting with the goal of natural image segmentation. We explore the biases of the spatial ddCRP model and propose a novel hierarchical extension better suited for producing “humanlike ” segmentations. We then study the sensitivity of the models to various distance and appearance hyperparameters, and provide the first rigorous comparison of nonparametric Bayesian models in the image segmentation domain. On unsupervised image segmentation, we demonstrate that similar performance to existing nonparametric Bayesian models is possible with substantially simpler models and algorithms. 1
Spectral Chinese Restaurant Processes: Nonparametric Clustering Based on Similarities
"... We introduce a new nonparametric clustering model which combines the recently proposed distancedependent Chinese restaurant process (ddCRP) and nonlinear, spectral methods for dimensionality reduction. Our model retains the ability of nonparametric methods to learn the number of clusters from dat ..."
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Cited by 11 (0 self)
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We introduce a new nonparametric clustering model which combines the recently proposed distancedependent Chinese restaurant process (ddCRP) and nonlinear, spectral methods for dimensionality reduction. Our model retains the ability of nonparametric methods to learn the number of clusters from data. At the same time it addresses two key limitations of nonparametric Bayesian methods: modeling data that are not exchangeable and have many correlated features. Spectral methods use the similarity between documents to map them into a lowdimensional spectral space where we then compare several clustering methods. Our experiments on handwritten digits and text documents show that nonparametric methods such as the CRP or ddCRP can perform as well as or better than kmeans and also recover the true number of clusters. We improve the performance of the ddCRP in spectral space by incorporating the original similarity matrix in its prior. This simple modification results in better performance than all other methods we compared to. We offer a new formulation and first experimental evaluation of a general Gibbs sampler for mixture modeling with distancedependent CRPs. 1
Bayesian inference with posterior regularization and applications to infinite latent svms
 In arXiv:1210.1766v2
, 2013
"... Existing Bayesian models, especially nonparametric Bayesian methods, rely on specially conceived priors to incorporate domain knowledge for discovering improved latent representations. While priors affect posterior distributions through Bayes ’ rule, imposing posterior regularization is arguably mo ..."
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Cited by 11 (6 self)
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Existing Bayesian models, especially nonparametric Bayesian methods, rely on specially conceived priors to incorporate domain knowledge for discovering improved latent representations. While priors affect posterior distributions through Bayes ’ rule, imposing posterior regularization is arguably more direct and in some cases more natural and general. In this paper, we present regularized Bayesian inference (RegBayes), a novel computational framework that performs posterior inference with a regularization term on the desired postdata posterior distribution under an information theoretical formulation. RegBayes is more flexible than the procedure that elicits expert knowledge via priors, and it covers both directed Bayesian networks and undirected Markov networks. When the regularization is induced from a linear operator on the posterior distributions, such as the expectation operator, we present a general convexanalysis theorem to characterize the solution of RegBayes. Furthermore, we present two concrete examples of RegBayes, infinite latent support vector machines (iLSVM) and multitask infinite latent support vector machines (MTiLSVM), which explore the largemargin idea in combination with a nonparametric Bayesian model for dis
Distance Dependent Infinite Latent Feature Models
, 2011
"... Latent feature models are widely used to decompose data into a small number of components. 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 generalizat ..."
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Cited by 10 (0 self)
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Latent feature models are widely used to decompose data into a small number of components. 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 (ddIBP), for modeling nonexchangeable data. It relies on a distance function defined between data points, biasing nearby data to share more features. The choice of distance function allows for many kinds of dependencies, including temporal or spatial. Further, the original IBP is a special case of the ddIBP. In this paper, we develop the ddIBP and theoretically characterize the distribution of how features are shared between data. We derive a Markov chain Monte Carlo sampler for a linear Gaussian model with a ddIBP prior and study its performance on several data sets for which exchangeability is not a reasonable assumption.
MultiClass Video CoSegmentation with a Generative MultiVideo Model
"... Video data provides a rich source of information that is available to us today in large quantities e.g. from online resources. Tasks like segmentation benefit greatly from the analysis of spatiotemporal motion patterns in videos and recent advances in video segmentation has shown great progress in ..."
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Cited by 9 (1 self)
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Video data provides a rich source of information that is available to us today in large quantities e.g. from online resources. Tasks like segmentation benefit greatly from the analysis of spatiotemporal motion patterns in videos and recent advances in video segmentation has shown great progress in exploiting these addition cues. However, observing a single video is often not enough to predict meaningful segmentations and inference across videos becomes necessary in order to predict segmentations that are consistent with objects classes. Therefore the task of video cosegmentation is being proposed, that aims at inferring segmentation from multiple videos. But current approaches are limited to only considering binary foreground/background segmentation and multiple videos of the same object. This is a clear mismatch to the challenges that we are facing with videos from online resources or consumer videos. We propose to study multiclass video cosegmentation where the number of object classes is unknown as well as the number of instances in each frame and video. We achieve this by formulating a nonparametric bayesian model across videos sequences that is based on a new videos segmentation prior as well as a global appearance model that links segments of the same class. We present the first multiclass video cosegmentation evaluation. We show that our method is applicable to real video data from online resources and outperforms stateoftheart video segmentation and image cosegmentation baselines. 1.
From Deformations to Parts: Motionbased Segmentation of 3D Objects
"... We develop a method for discovering the parts of an articulated object from aligned meshes of the object in various threedimensional poses. We adapt the distance dependent Chinese restaurant process (ddCRP) to allow nonparametric discovery of a potentially unbounded number of parts, while simultane ..."
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Cited by 5 (3 self)
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We develop a method for discovering the parts of an articulated object from aligned meshes of the object in various threedimensional poses. We adapt the distance dependent Chinese restaurant process (ddCRP) to allow nonparametric discovery of a potentially unbounded number of parts, while simultaneously guaranteeing a spatially connected segmentation. To allow analysis of datasets in which object instances have varying 3D shapes, we model part variability across poses via affine transformations. By placing a matrix normalinverseWishart prior on these affine transformations, we develop a ddCRP Gibbs sampler which tractably marginalizes over transformation uncertainty. Analyzing a dataset of humans captured in dozens of poses, we infer parts which provide quantitatively better deformation predictions than conventional clustering methods. 1
Inferring Interaction Networks using the IBP applied to microRNA Target Prediction
"... Determining interactions between entities and the overall organization and clustering of nodes in networks is a major challenge when analyzing biological and social network data. Here we extend the Indian Buffet Process (IBP), a nonparametric Bayesian model, to integrate noisy interaction scores wit ..."
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Cited by 4 (0 self)
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Determining interactions between entities and the overall organization and clustering of nodes in networks is a major challenge when analyzing biological and social network data. 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 nodes within these networks. We present an application of this method to study how microRNAs regulate mRNAs in cells. Analysis of synthetic and real data indicates that the method improves upon prior methods, correctly recovers interactions and clusters, and provides accurate biological predictions. 1