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A Nested HDP for Hierarchical Topic Models
[article]
2013
arXiv
pre-print
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We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. ...
The nHDP is a generalization of the nested Chinese restaurant process (nCRP) that allows each word to follow its own path to a topic node according to a document-specific distribution on a shared tree. ...
Dirichlet process (nHDP) for hierarchical topic modeling. ...
arXiv:1301.3570v1
fatcat:fzhsuxehgvasjayfkpyy7pazfu
Nested Hierarchical Dirichlet Process for Nonparametric Entity-Topic Analysis
[chapter]
2013
Lecture Notes in Computer Science
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Making use of a nested Chinese Restaurant Franchise (nCRF) representation for the nested HDP, we propose a collapsed Gibbs sampling based inference algorithm for the model. ...
The Hierarchical Dirichlet Process (HDP) is a Bayesian nonparametric prior for grouped data, such as collections of documents, where each group is a mixture of a set of shared mixture densities, or topics ...
Nonparametric Entity-Topic Analysis We now present our nested Hierarchical Dirichlet Process (nHDP) model for nonparametric entity-topic analysis. ...
doi:10.1007/978-3-642-40991-2_36
fatcat:5aehsmfoorbrvlbbkfj4ias5de
The Hybrid Nested/Hierarchical Dirichlet Process and its Application to Topic Modeling with Word Differentiation
2015
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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The hierarchical Dirichlet process (HDP) is a powerful nonparametric Bayesian approach to modeling groups of data which allows the mixture components in each group to be shared. ...
In order to utilize the unknown category information of grouped data, we present the hybrid nested/ hierarchical Dirichlet process (hNHDP), a prior that blends the desirable aspects of both the HDP and ...
The Hybrid Nested/Hierarchical Dirichlet Process We propose the hybrid nested/hierarchical Dirichlet process (hNHDP) mixture model for groups of data. ...
doi:10.1609/aaai.v29i1.9591
fatcat:ioxcynbyarav3ijfnaptumz4pi
Nested Hierarchical Dirichlet Processes for Multi-Level Non-Parametric Admixture Modeling
[article]
2015
arXiv
pre-print
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The Hierarchical Dirichlet Process (HDP), is an extension of DP for grouped data, often used for non-parametric topic modeling, where each group is a mixture over shared mixture densities. ...
We evaluate our model with the two level nHDP for non-parametric entity topic modeling where an inner HDP creates a countably infinite set of topic mixtures and associates them with author entities, while ...
Conclusions In this paper, we have proposed the the nested Hierarchical Dirichlet Process as a prior for multi-level admixture modeling. ...
arXiv:1508.06446v2
fatcat:sdddi4wlqnbylez5lcxeaq5fr4
Learning Conditional Latent Structures from Multiple Data Sources
[chapter]
2015
Lecture Notes in Computer Science
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The proposed framework, first, induces mixture distribution over primary data source using hierarchical Dirichlet processes (HDP). ...
To address this problem, we propose a full Bayesian nonparametric approach to model correlation structures among multiple and heterogeneous datasets. ...
The model can be viewed as a generalization of the hierarchical Dirichlet process (HDP) [14] and the nested Dirichlet process (nDP) [12] . ...
doi:10.1007/978-3-319-18038-0_27
fatcat:t4p43t5rprdm3ek64ffhe57zsm
Nested Hierarchical Dirichlet Processes
2015
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. ...
We derive a stochastic variational inference algorithm for the model, in addition to a greedy subtree selection method for each document, which allows for efficient inference using massive collections ...
In Section 3 we present our proposed nested HDP model for hierarchical topic modeling. ...
doi:10.1109/tpami.2014.2318728
pmid:26353240
fatcat:6wv55p33mferte5hpuecrwuelm
Learning to Learn with Compound HD Models
2011
Neural Information Processing Systems
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We introduce HD (or "Hierarchical-Deep") models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian models. ...
Specifically we show how we can learn a hierarchical Dirichlet process (HDP) prior over the activities of the top-level features in a Deep Boltzmann Machine (DBM). ...
For a fixed number of topics T , the above model represents a hierarchical extension of LDA. We typically do not know the number of topics a-priori. ...
dblp:conf/nips/SalakhutdinovTT11
fatcat:zgmboicurrcntkx2hih33ylhmm
Nonparametric Bayes Pachinko Allocation
[article]
2012
arXiv
pre-print
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In this paper, we propose a nonparametric Bayesian prior for PAM based on a variant of the hierarchical Dirichlet process (HDP). ...
For example, the pachinko allocation model (PAM) captures arbitrary, nested, and possibly sparse correlations between topics using a directed acyclic graph (DAG). ...
Acknowledgements This work was supported in part by the Center for Intelligent Information Retrieval and in part by the Defense Advanced Research Projects Agency (DARPA), through the Department of the ...
arXiv:1206.5270v1
fatcat:p4nby4ymorfonpugum6kycpuca
Sampling Table Configurations for the Hierarchical Poisson-Dirichlet Process
[chapter]
2011
Lecture Notes in Computer Science
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responsibility for starting a new table. ...
Hierarchical modeling and reasoning are fundamental in machine intelligence, and for this the two-parameter Poisson-Dirichlet Process (PDP) plays an important role. ...
One particular use of the PDP/DP is in the area of topic models where hierarchical PDPs and hierarchical DPs provide elegant machinery for improving the standard simple topic model [12, 13] , for instance ...
doi:10.1007/978-3-642-23780-5_29
fatcat:c5lwhry3r5herkgtfvdzoucl74
Pachinko allocation
2006
Proceedings of the 23rd international conference on Machine learning - ICML '06
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In this paper, we introduce the pachinko allocation model (PAM), which captures arbitrary, nested, and possibly sparse correlations between topics using a directed acyclic graph (DAG). ...
Latent Dirichlet allocation (LDA) and other related topic models are increasingly popular tools for summarization and manifold discovery in discrete data. ...
methods, and Michael Jordan for help naming the model. ...
doi:10.1145/1143844.1143917
dblp:conf/icml/LiM06
fatcat:7oem7ctk3bbfzaqa225oryjj6i
Coupled Hierarchical Dirichlet Process Mixtures for Simultaneous Clustering and Topic Modeling
[chapter]
2016
Lecture Notes in Computer Science
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We propose a nonparametric Bayesian mixture model that simultaneously optimizes the topic extraction and group clustering while allowing all topics to be shared by all clusters for grouped data. ...
Experimental results with corpus data show that our model has a better performance than existing models, achieving a 22% improvement against state-of-the-art model. ...
We thank Tengfei Ma, Issei Sato, and Hiroshi Nakagawa for providing the hNHDP implementation. This work was partly supported by CREST, JST. ...
doi:10.1007/978-3-319-46227-1_15
fatcat:ggy3hygq2jeblcfzr2z2ndsokm
Local-HDP: Interactive Open-Ended 3D Object Categorization in Real-Time Robotic Scenarios
[article]
2021
arXiv
pre-print
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We introduce a non-parametric hierarchical Bayesian approach for open-ended 3D object categorization, named the Local Hierarchical Dirichlet Process (Local-HDP). ...
Moreover, fixing the number of topics for all categories can lead to overfitting or underfitting of the model. ...
A video for this robotic demonstration is available at: https://youtu.be/otxd8D8yYLc
Conclusion We propose a non-parametric hierarchical Bayesian model called Local Hierarchical Dirichlet Process (Local-HDP ...
arXiv:2009.01152v3
fatcat:k34ix4af6vcaznhwdfl5malgbi
Learning with Hierarchical-Deep Models
2013
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We introduce HD (or "Hierarchical-Deep") models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian (HB) models. ...
Specifically, we show how we can learn a hierarchical Dirichlet process (HDP) prior over the activities of the top-level features in a deep Boltzmann machine (DBM). ...
In our compound HDP-DBM model, we will use a hierarchical topic model as a prior over the activities of the DBM's top-level features. ...
doi:10.1109/tpami.2012.269
pmid:23787346
fatcat:ecdmqr225nfati75px3fbcocaa
Author Tree-Structured Hierarchical Dirichlet Process
[chapter]
2018
Lecture Notes in Computer Science
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We introduce the Author Tree-structured Hierarchical Dirichlet Process (ATHDP), allowing Dirichlet process based topic modeling of both text content and authors over a given tree structure of arbitrary ...
However, traditional models such as Hierarchical Dirichlet Processes (HDPs) do not fully take into account authors, and are further unable to fully take into account deep hierarchical venues where documents ...
Author Tree-structured Hierarchical Dirichlet Process and the flexibility of the model could be further increased by, for example, modeling within-topic correlations between authors and word content, or ...
doi:10.1007/978-3-030-01771-2_20
fatcat:wtukx3eanjc53m5p2qbmbk2fg4
Modeling topic hierarchies with the recursive chinese restaurant process
2012
Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12
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We introduce the recursive Chinese restaurant process (rCRP) and a nonparametric topic model with rCRP as a prior for discovering a hierarchical topic structure with unbounded depth and width. ...
Topic models such as latent Dirichlet allocation (LDA) and hierarchical Dirichlet processes (HDP) are simple solutions to discover topics from a set of unannotated documents. ...
We propose a novel prior, recursive Chinese restaurant process (rCRP), and a hierarchical topic model with rCRP as a prior that can handle such flexible hierarchical topic modeling. ...
doi:10.1145/2396761.2396861
dblp:conf/cikm/KimKKO12
fatcat:fjaids4sf5d5npwo7wybqxjx4e
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