Author name cluster

Akisato Kimura

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

2 author rows

NeurIPS Conference 2021 Conference Paper

Permuton-induced Chinese Restaurant Process

Masahiro Nakano
Yasuhiro Fujiwara
Akisato Kimura
Takeshi Yamada
Naonori Ueda

This paper proposes the permuton-induced Chinese restaurant process (PCRP), a stochastic process on rectangular partitioning of a matrix. This distribution is suitable for use as a prior distribution in Bayesian nonparametric relational model to find hidden clusters in matrices and network data. Our main contribution is to introduce the notion of permutons into the well-known Chinese restaurant process (CRP) for sequence partitioning: a permuton is a probability measure on $[0, 1]\times [0, 1]$ and can be regarded as a geometric interpretation of the scaling limit of permutations. Specifically, we extend the model that the table order of CRPs has a random geometric arrangement on $[0, 1]\times [0, 1]$ drawn from the permuton. By analogy with the relationship between the stick-breaking process (SBP) and CRP for the infinite mixture model of a sequence, this model can be regarded as a multi-dimensional extension of CRP paired with the block-breaking process (BBP), which has been recently proposed as a multi-dimensional extension of SBP. While BBP always has an infinite number of redundant intermediate variables, PCRP can be composed of varying size intermediate variables in a data-driven manner depending on the size and quality of the observation data. Experiments show that PCRP can improve the prediction performance in relational data analysis by reducing the local optima and slow mixing problems compared with the conventional BNP models because the local transitions of PCRP in Markov chain Monte Carlo inference are more flexible than the previous models.

PDF Details

NeurIPS Conference 2020 Conference Paper

Baxter Permutation Process

Masahiro Nakano
Akisato Kimura
Takeshi Yamada
Naonori Ueda

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 floorplan partitioning (FP), which constitutes a subset of rectangular partitioning (RP). Accordingly, the BPP can be used as an FP model. We combine the BPP with a multi-dimensional extension of the stick-breaking process called the {\it block-breaking process} to fill the gap between FP and RP, and obtain a stochastic process on arbitrary RPs. Compared with conventional BNP models for arbitrary RPs, the proposed model is simpler and has a high affinity with Bayesian inference.

PDF Details

AAAI Conference 2018 Conference Paper

Weakly Supervised Collective Feature Learning From Curated Media

Yusuke Mukuta
Akisato Kimura
David Adrian
Zoubin Ghahramani

The current state-of-the-art in feature learning relies on the supervised learning of large-scale datasets consisting of target content items and their respective category labels. However, constructing such large-scale fully-labeled datasets generally requires painstaking manual effort. One possible solution to this problem is to employ community contributed text tags as weak labels, however, the concepts underlying a single text tag strongly depends on the users. We instead present a new paradigm for learning discriminative features by making full use of the human curation process on social networking services (SNSs). During the process of content curation, SNS users collect content items manually from various sources and group them by context, all for their own beneﬁt. Due to the nature of this process, we can assume that (1) content items in the same group share the same semantic concept and (2) groups sharing the same images might have related semantic concepts. Through these insights, we can deﬁne human curated groups as weak labels from which our proposed framework can learn discriminative features as a representation in the space of semantic concepts the users intended when creating the groups. We show that this feature learning can be formulated as a problem of link prediction for a bipartite graph whose nodes corresponds to content items and human curated groups, and propose a novel method for feature learning based on sparse coding or network ﬁne-tuning.

PDF Details

AAAI Conference 2016 Conference Paper

Infinite Plaid Models for Infinite Bi-Clustering

Katsuhiko Ishiguro
Issei Sato
Masahiro Nakano
Akisato Kimura
Naonori Ueda

We propose a probabilistic model for non-exhaustive and overlapping (NEO) bi-clustering. Our goal is to extract a few sub-matrices from the given data matrix, where entries of a sub-matrix are characterized by a speciﬁc distribution or parameters. Existing NEO bi-clustering methods typically require the number of sub-matrices to be extracted, which is essentially diﬃcult to ﬁx a priori. In this paper, we extend the plaid model, known as one of the best NEO bi-clustering algorithms, to allow inﬁnite bi-clustering; NEO bi-clustering without specifying the number of sub-matrices. Our model can represent inﬁnite sub-matrices formally. We develop a MCMC inference without the ﬁnite truncation, which potentially addresses all possible numbers of sub-matrices. Experiments quantitatively and qualitatively verify the usefulness of the proposed model. The results reveal that our model can oﬀer more precise and in-depth analysis of sub-matrices.

PDF Details

ICML Conference 2014 Conference Paper

Rectangular Tiling Process

Masahiro Nakano
Katsuhiko Ishiguro
Akisato Kimura
Takeshi Yamada
Naonori Ueda

This paper proposes a novel stochastic process that represents the arbitrary rectangular partitioning of an infinite-dimensional matrix as the conditional projective limit. Rectangular partitioning is used in relational data analysis, and is classified into three types: regular grid, hierarchical, and arbitrary. Conventionally, a variety of probabilistic models have been advanced for the first two, including the product of Chinese restaurant processes and the Mondrian process. However, existing models for arbitrary partitioning are too complicated to permit the analysis of the statistical behaviors of models, which places very severe capability limits on relational data analysis. In this paper, we propose a new probabilistic model of arbitrary partitioning called the rectangular tiling process (RTP). Our model has a sound mathematical base in projective systems and infinite extension of conditional probabilities, and is capable of representing partitions of infinite elements as found in ordinary Bayesian nonparametric models.

Details

IJCAI Conference 2013 Conference Paper

Change-Point Detection with Feature Selection in High-Dimensional Time-Series Data

Makoto Yamada
Akisato Kimura
Futoshi Naya
Hiroshi Sawada

Change-point detection is the problem of finding abrupt changes in time-series, and it is attracting a lot of attention in the artificial intelligence and data mining communities. In this paper, we present a supervised learning based change-point detection approach in which we use the separability of past and future data at time t (they are labeled as +1 and 1) as plausibility of change-points. Based on this framework, we propose a detection measure called the additive Hilbert-Schmidt Independence Criterion (aHSIC), which is defined as the weighted sum of the HSIC scores between features and its corresponding binary labels. Here, the HSIC is a kernelbased independence measure. A novel aspect of the aHSIC score is that it can incorporate feature selection during its detection measure estimation. More specifically, we first select features that are responsible for an abrupt change by using a supervised approach, and then compute the aHSIC score by employing the selected features. Thus, compared with traditional detection measures, our approach tends to be robust as regards noise features, and so the aHSIC is suitable for a use with high-dimensional time-series change-point detection problems. We demonstrate that the proposed change-point detection method is promising through extensive experiments on synthetic data sets and a real-world human activity data set.

PDF Details DOI

IJCAI Conference 2013 Conference Paper

Non-Negative Multiple Matrix Factorization

Koh Takeuchi
Katsuhiko Ishiguro
Akisato Kimura
Hiroshi Sawada

Non-negative Matrix Factorization (NMF) is a traditional unsupervised machine learning technique for decomposing a matrix into a set of bases and coeﬃcients under the non-negative constraint. NMF with sparse constraints is also known for extracting reasonable components from noisy data. However, NMF tends to give undesired results in the case of highly sparse data, because the information included in the data is insuﬃcient to decompose. Our key idea is that we can ease this problem if complementary data are available that we could integrate into the estimation of the bases and coeﬃcients. In this paper, we propose a novel matrix factorization method called Non-negative Multiple Matrix Factorization (NMMF), which utilizes complementary data as auxiliary matrices that share the row or column indices of the target matrix. The data sparseness is improved by decomposing the target and auxiliary matrices simultaneously, since auxiliary matrices provide information about the bases and coeﬃcients. We formulate NMMF as a generalization of NMF, and then present a parameter estimation procedure derived from the multiplicative update rule. We examined NMMF in both synthetic and real data experiments. The eﬀect of the auxiliary matrices appeared in the improved NMMF performance. We also conﬁrmed that the bases that NMMF obtained from the real data were intuitive and reasonable thanks to the non-negative constraint.

PDF Details DOI