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Yuto Yamaguchi

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3 papers

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IJCAI Conference 2017 Conference Paper

Tensor Decomposition with Missing Indices

Yuto Yamaguchi
Kohei Hayashi

How can we decompose a data tensor if the indices are partially missing? Tensor decomposition is a fundamental tool to analyze the tensor data. Suppose, for example, we have a 3rd-order tensor X where each element Xijk takes 1 if user i posts word j at location k on Twitter. Standard tensor decomposition expects all the indices are observed but, in some tweets, location k can be missing. In this paper, we study a tensor decomposition problem where the indices (i, j, or k) of some observed elements are partially missing. Towards the problem, we propose a probabilistic tensor decomposition model that handles missing indices as latent variables. To infer them, we derive an algorithm based on stochastic variational inference, which enables to leverage the information from the incomplete data scalably. The experiments on both synthetic and real datasets show that the proposed method achieves higher accuracy in the tensor completion task than baselines that cannot handle missing indices.

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IJCAI Conference 2017 Conference Paper

When Does Label Propagation Fail? A View from a Network Generative Model

Yuto Yamaguchi
Kohei Hayashi

What kinds of data does Label Propagation (LP) work best on? Can we justify the solution of LP from a theoretical standpoint? LP is a semi-supervised learning algorithm that is widely used to predict unobserved node labels on a network (e. g. , user's gender on an SNS). Despite its importance, its theoretical properties remain mostly unexplored. In this paper, we answer the above questions by interpreting LP from a statistical viewpoint. As our main result, we identify the network generative model behind the discretized version of LP (DLP), and we show that under specific conditions the solution of DLP is equal to the maximum {\it a posteriori} estimate of that generative model. Our main result reveals the critical limitations of LP. Specifically, we discover that LP would not work best on networks with (1) disassortative node labels, (2) clusters having different edge densities, (3) non-uniform label distributions, or (4) unreliable node labels provided. Our experiments under a variety of settings support our theoretical results.

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AAAI Conference 2015 Conference Paper

OMNI-Prop: Seamless Node Classification on Arbitrary Label Correlation

Yuto Yamaguchi
Christos Faloutsos
Hiroyuki Kitagawa

If we know most of Smith’s friends are from Boston, what can we say about the rest of Smith’s friends? In this paper, we focus on the node classification problem on networks, which is one of the most important topics in AI and Web communities. Our proposed algorithm which is referred to as OMNI- Prop has the following properties: (a) seamless and accurate; it works well on any label correlations (i. e. , homophily, heterophily, and mixture of them) (b) fast; it is efficient and guaranteed to converge on arbitrary graphs (c) quasi-parameter free; it has just one well-interpretable parameter with heuristic default value of 1. We also prove the theoretical connections of our algorithm to the semi-supervised learning (SSL) algorithms and to random-walks. Experiments on four real, different network datasets demonstrate the benefits of the proposed algorithm, where OMNI-Prop outperforms the top competitors.

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