Infinite Author Topic Model Based on Mixed Gamma-Negative Binomial Process

Xuan, Junyu; Lu, Jie; Zhang, Guangquan; Xu, Richard Yi Da; Luo, Xiangfeng

doi:10.1109/icdm.2015.19

Cited by 10 publications

(6 citation statements)

References 27 publications

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“…Further, by using dependent GaPs or a mixed GaP-NBP, additional information from the corpus can be introduced into a model. [191] incorporated the links (e.g., citations between scientific papers) between the documents using a dependent GaPs and authorship information using a mixed GaP-NBP [190]. -Multiple corpora.…”

Section: Text Miningmentioning

confidence: 99%

A Survey on Bayesian Nonparametric Learning

Xuan

Zhang

2019

ACM Comput. Surv.

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Bayesian (machine) learning has been playing a significant role in machine learning for a long time due to its particular ability to embrace uncertainty, encode prior knowledge, and endow interpretability. On the back of Bayesian learning's great success, Bayesian nonparametric learning (BNL) has emerged as a force for further advances in this field due to its greater modelling flexibility and representation power. Instead of playing with the fixed-dimensional probabilistic distributions of Bayesian learning, BNL creates a new "game" with infinite-dimensional stochastic processes. BNL has long been recognised as a research subject in statistics and, to date, several state-of-the-art pilot studies have demonstrated that BNL has a great deal of potential to solve real-world machine learning tasks. However, despite these promising results, BNL has not created a huge wave in the machine learning community. Esotericism may account for this. The books and surveys on BNL written by statisticians are overcomplicated and filled with tedious theories and proofs. Each is certainly meaningful but may scare away new researchers, especially those with computer science backgrounds. Hence, the aim of this paper is to provide a plain-spoken, yet comprehensive, theoretical survey of BNL in terms that researchers in the machine learning community can understand. It is hoped this survey will serve as a starting point for understanding and exploiting the benefits of BNL in our current scholarly endeavours. To achieve this goal, we have collated the extant studies in this field and aligned them with the steps of a standard BNL procedure-from selecting the appropriate stochastic processes, through manipulation, to executing the model inference algorithms. At each step, past efforts have been thoroughly summarised and discussed. In addition, we have reviewed the common methods for implementing BNL in various machine learning tasks along with its diverse applications in the real-world as examples to motivate future studies.

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Section: Text Miningmentioning

confidence: 99%

A Survey on Bayesian Nonparametric Learning

Xuan

Zhang

2019

ACM Comput. Surv.

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“…On the other hand, exploring hidden information from observed data have been common desired in applications of data mining like recommend systems [18], information retrieval [32], statistical natural language processing [16] and so on. Among them, probabilistic graphical models are widely used since its huge success in classifying topics from contexts [3].…”

Section: Related Workmentioning

confidence: 99%

Latent Dirichlet Allocation for Internet Price War

Yan

Deng

et al. 2019

AAAI

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Internet market makers are always facing intense competitive environment, where personalized price reductions or discounted coupons are provided for attracting more customers. Participants in such a price war scenario have to invest a lot to catch up with other competitors. However, such a huge cost of money may not always lead to an improvement of market share. This is mainly due to a lack of information about others' strategies or customers' willingness when participants develop their strategies.In order to obtain this hidden information through observable data, we study the relationship between companies and customers in the Internet price war. Theoretically, we provide a formalization of the problem as a stochastic game with imperfect and incomplete information. Then we develop a variant of Latent Dirichlet Allocation (LDA) to infer latent variables under the current market environment, which represents the preferences of customers and strategies of competitors. To our best knowledge, it is the first time that LDA is applied to game scenario.We conduct simulated experiments where our LDA model exhibits a significant improvement on finding strategies in the Internet price war by including all available market information of the market maker's competitors. And the model is applied to an open dataset for real business. Through comparisons on the likelihood of prediction for users' behavior and distribution distance between inferred opponent's strategy and the real one, our model is shown to be able to provide a better understanding for the market environment.Our work marks a successful learning method to infer latent information in the environment of price war by the LDA modeling, and sets an example for related competitive applications to follow.

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“…A similar idea was adopted in the gamma-negative binomial process [43,44], beta-negative binomial process [45], hierarchical beta process [46] and hierarchical Poisson models [47]. Different stochastic processes, e.g., beta, Gamma, Poisson and negative binomial processes, used in these models are piled to account for different kinds of data (i.e., binary or count data) in the hierarchical structure.…”

Section: Learning From Hierarchical Structures Using Bayesian Nonpara...mentioning

confidence: 99%

Cooperative hierarchical Dirichlet processes: Superposition vs. maximization

Xuan

Zhang

2019

Artificial Intelligence

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The cooperative hierarchical structure is a common and significant data structure observed in, or adopted by, many research areas, such as: text mining (author-paper-word) and multi-label classification (label-instance-feature).Renowned Bayesian approaches for cooperative hierarchical structure modeling are mostly based on topic models. However, these approaches suffer from a serious issue in that the number of hidden topics/factors needs to be fixed in advance and an inappropriate number may lead to overfitting or underfitting.One elegant way to resolve this issue is Bayesian nonparametric learning, but existing work in this area still cannot be applied to cooperative hierarchical structure modeling.In this paper, we propose a cooperative hierarchical Dirichlet process (CHDP) to fill this gap. Each node in a cooperative hierarchical structure is assigned a Dirichlet process to model its weights on the infinite hidden factors/topics. Together with measure inheritance from hierarchical Dirichlet process, two kinds of measure cooperation, i.e., superposition and maximization, are defined to capture the many-to-many relationships in the cooperative hierarchical structure. Furthermore, two constructive representations for CHDP, i.e., stick-breaking and international restaurant process, are designed to facilitate the model inference. Experiments on synthetic and real-world data with cooperative hierarchical structures demonstrate the properties and the ability of CHDP for cooperative hierarchical structure modeling and its potential for practical application scenarios.A hierarchical structure has multiple layers, and each layer contains a number of nodes that are linked to the nodes in the higher and lower layers, as illustrated in Figure 1. This kind of structure is very common and pervasive, and has been adopted in many different sub-fields in the artificial intelligence 5 area. One example of such structure is found in text mining. Consider all the papers in a scientific journal (e.g., Artificial Intelligence). An author-paper-word[1] hierarchical structure emerges, given each author writes and publishes a number of scientific papers in this journal, and each paper is composed of several different words. Learning from author-paper-word structure is useful for collab-10 orators' recommendations, authors disambiguation, paper clustering, statistical machine translation [2], and so on. Another example occurs within image processing. The scene-image-feature hierarchical structure is formed because each image may belong to several scenes, such as beach or urban [3], and an image is also described by an abundance of features, such as grayscale and texture. 15Learning from scene-image-feature structure could at least benefit image search and context-sensitive image enhancement.Current state-of-the-art Bayesian approaches to learn from this hierarchical structure are mainly based on topic models [4, 5] that are a kind of probabilistic graphical models [6] and were originally designed for modeling a two-level hier-20 ar...

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Infinite Author Topic Model Based on Mixed Gamma-Negative Binomial Process

Cited by 10 publications

References 27 publications

A Survey on Bayesian Nonparametric Learning

A Survey on Bayesian Nonparametric Learning

Latent Dirichlet Allocation for Internet Price War

Cooperative hierarchical Dirichlet processes: Superposition vs. maximization

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