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Our main contributions are as follows: We have presented the Bayesian nonparametric model of the Baxter permutations as a Markov process consisting of a sequence of i.i.d. uniform random variables on

Baxter Permutation Process

NIPS 2020, (2020)

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Abstract

In this paper, a Bayesian nonparametric (BNP) model for Baxter permutations (BPs), termed BP process (BPP) is proposed and applied to relational data analysis. The BPs are a well-studied class of permutations, and it has been demonstrated that there is one-to-one correspondence between BPs and several interesting objects including floorpl...More

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Introduction
  • Bayesian nonparametric (BNP) methods can overcome the model complexity problem of machine learning tasks, as they can be regarded as an analysis of finite subsets of potentially infinite data using infinite-dimensional probabilistic models, i.e., stochastic processes.
  • The authors develop a BNP model of Baxter permutations (BPs)
  • This model involves new stochastic processes and is applied to relational data analysis.
  • For RP models, the infinite relational model (IRM) [33] and the Mondrian process (MP) [49, 48] have been widely studied and applied to real world applications
  • These models cannot represent arbitrary RPs. these models cannot represent arbitrary RPs
  • That is, their supports are limited to some subsets of all possible RPs (Figure 1, second and third).
  • It has too complicated procedures for the model construction due to its projectivity property, and is not well-suited for Bayesian inference
Highlights
  • Bayesian nonparametric (BNP) methods can overcome the model complexity problem of machine learning tasks, as they can be regarded as an analysis of finite subsets of potentially infinite data using infinite-dimensional probabilistic models, i.e., stochastic processes
  • In order to describe the evolution of the Baxter permutation (BP) process (BPP), we introduce auxiliary variables, consisting of a sequence of independent and identically distributed (i.i.d.) uniform random variables U1, U2, . . . on [0, 1]
  • We introduce a sequence of i.i.d beta random variables into the BPP to control the size of the rooms of the floorplan partitioning (FP) drawn from the BPP
  • Our main contributions are as follows: (1) We have presented the BNP model of the BP as a Markov process consisting of a sequence of i.i.d. uniform random variables on [0, 1]
  • Owing to the one-to-one correspondence between BP and FP, the model can be used as a probabilistic model on the set of all possible FPs. (2) We combined the BPP with the block-breaking process (BBP) to obtain a stochastic process for arbitrary rectangular partitioning (RP)
  • The blockbreaking process (BBP) can be regarded as a multi-dimensional extension of clustering and it has a potential to give a new perspective to relational data analysis, for it would reveal latent structures in relational data in much more flexible manner than other existing clustering methods, without tuning the model complexity
Results
  • The authors held out 20% cells of the input data for testing, and each model was trained by the MCMC using the remaining 80% of the cells.
Conclusion
  • This paper has proposed new stochastic processes. The authors' main contributions are as follows: (1) The authors have presented the BNP model of the BP as a Markov process consisting of a sequence of i.i.d. uniform random variables on [0, 1].
  • (2) The authors combined the BPP with the BBP to obtain a stochastic process for arbitrary RPs. As in conventional methods, the authors applied this process to the AHK representation to construct a BNP stochastic block model for relational data, and compared its predictive performance with that of the IRM, MP, and RTP.
  • The blockbreaking process (BBP) can be regarded as a multi-dimensional extension of clustering and it has a potential to give a new perspective to relational data analysis, for it would reveal latent structures in relational data in much more flexible manner than other existing clustering methods, without tuning the model complexity
Summary
  • Introduction:

    Bayesian nonparametric (BNP) methods can overcome the model complexity problem of machine learning tasks, as they can be regarded as an analysis of finite subsets of potentially infinite data using infinite-dimensional probabilistic models, i.e., stochastic processes.
  • The authors develop a BNP model of Baxter permutations (BPs)
  • This model involves new stochastic processes and is applied to relational data analysis.
  • For RP models, the infinite relational model (IRM) [33] and the Mondrian process (MP) [49, 48] have been widely studied and applied to real world applications
  • These models cannot represent arbitrary RPs. these models cannot represent arbitrary RPs
  • That is, their supports are limited to some subsets of all possible RPs (Figure 1, second and third).
  • It has too complicated procedures for the model construction due to its projectivity property, and is not well-suited for Bayesian inference
  • Objectives:

    Contributions - The aim of this paper is to construct a new BNP model for arbitrary RPs, so that it has a simple description and high affinity with Bayesian inference.
  • Results:

    The authors held out 20% cells of the input data for testing, and each model was trained by the MCMC using the remaining 80% of the cells.
  • Conclusion:

    This paper has proposed new stochastic processes. The authors' main contributions are as follows: (1) The authors have presented the BNP model of the BP as a Markov process consisting of a sequence of i.i.d. uniform random variables on [0, 1].
  • (2) The authors combined the BPP with the BBP to obtain a stochastic process for arbitrary RPs. As in conventional methods, the authors applied this process to the AHK representation to construct a BNP stochastic block model for relational data, and compared its predictive performance with that of the IRM, MP, and RTP.
  • The blockbreaking process (BBP) can be regarded as a multi-dimensional extension of clustering and it has a potential to give a new perspective to relational data analysis, for it would reveal latent structures in relational data in much more flexible manner than other existing clustering methods, without tuning the model complexity
Tables
  • Table1: Perplexity comparison for real-world relational data analysis (mean±std)
Download tables as Excel
Funding
  • Funding disclosure Funding in direct support of this work is from NTT Corporation, without any third party funding.
Study subjects and analysis
social network datasets: 4
Each matrix consists of 300 × 300 binary elements drawn from the beta-Bernoulli likelihood model. We also used four social network datasets [54, 35] (corresponding to Figure 1):. • Wiki (top-left) [1], consisting of 7115 nodes and 103689 edges with diameter 7

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