Search Results for author: Hiroyuki Kasai

Found 28 papers, 13 papers with code

Wasserstein Weisfeiler-Lehman Subtree Distance for Graph-Structured Data

no code implementations29 Sep 2021 Zhongxi Fang, Jianming Huang, Hiroyuki Kasai

Therefore, instead of using such categorical labels, we define a node distance between WL subtrees with tree edit distance and propose an efficient calculation algorithm.

Graph Classification

Fast block-coordinate Frank-Wolfe algorithm for semi-relaxed optimal transport

no code implementations10 Mar 2021 Takumi Fukunaga, Hiroyuki Kasai

To this end, addressing a convex semi-relaxed OT, we propose a fast block-coordinate Frank-Wolfe (BCFW) algorithm, which gives sparse solutions.

Manifold optimization for non-linear optimal transport problems

1 code implementation1 Mar 2021 Bamdev Mishra, N T V Satyadev, Hiroyuki Kasai, Pratik Jawanpuria

In this work, we discuss how to computationally approach general non-linear OT problems within the framework of Riemannian manifold optimization.

LCS Graph Kernel Based on Wasserstein Distance in Longest Common Subsequence Metric Space

1 code implementation7 Dec 2020 Jianming Huang, Zhongxi Fang, Hiroyuki Kasai

Therefore, we propose a novel metric space by exploiting the proposed LCS-based similarity, and compute a new Wasserstein-based graph distance in this metric space, which emphasizes more the comparison between similar paths.

Graph Classification Graph Learning

Consistency-aware and Inconsistency-aware Graph-based Multi-view Clustering

2 code implementations25 Nov 2020 Mitsuhiko Horie, Hiroyuki Kasai

Among existing approaches, graph-based multi-view clustering (GMVC) achieves state-of-the-art performance by leveraging a shared graph matrix called the unified matrix.

Wasserstein k-means with sparse simplex projection

1 code implementation25 Nov 2020 Takumi Fukunaga, Hiroyuki Kasai

This paper presents a proposal of a faster Wasserstein $k$-means algorithm for histogram data by reducing Wasserstein distance computations and exploiting sparse simplex projection.

Graph embedding using multi-layer adjacent point merging model

no code implementations28 Oct 2020 Jianming Huang, Hiroyuki Kasai

For graph classification tasks, many traditional kernel methods focus on measuring the similarity between graphs.

Classification General Classification +2

Riemannian optimization on the simplex of positive definite matrices

no code implementations25 Jun 2019 Bamdev Mishra, Hiroyuki Kasai, Pratik Jawanpuria

In this work, we generalize the probability simplex constraint to matrices, i. e., $\mathbf{X}_1 + \mathbf{X}_2 + \ldots + \mathbf{X}_K = \mathbf{I}$, where $\mathbf{X}_i \succeq 0$ is a symmetric positive semidefinite matrix of size $n\times n$ for all $i = \{1,\ldots, K \}$.

Riemannian optimization

Riemannian joint dimensionality reduction and dictionary learning on symmetric positive definite manifold

no code implementations11 Feb 2019 Hiroyuki Kasai, Bamdev Mishra

Dictionary leaning (DL) and dimensionality reduction (DR) are powerful tools to analyze high-dimensional noisy signals.

Classification Dictionary Learning +4

Riemannian adaptive stochastic gradient algorithms on matrix manifolds

1 code implementation4 Feb 2019 Hiroyuki Kasai, Pratik Jawanpuria, Bamdev Mishra

We propose novel stochastic gradient algorithms for problems on Riemannian matrix manifolds by adapting the row and column subspaces of gradients.

Inexact trust-region algorithms on Riemannian manifolds

1 code implementation NeurIPS 2018 Hiroyuki Kasai, Bamdev Mishra

We consider an inexact variant of the popular Riemannian trust-region algorithm for structured big-data minimization problems.

McTorch, a manifold optimization library for deep learning

1 code implementation3 Oct 2018 Mayank Meghwanshi, Pratik Jawanpuria, Anoop Kunchukuttan, Hiroyuki Kasai, Bamdev Mishra

In this paper, we introduce McTorch, a manifold optimization library for deep learning that extends PyTorch.

Riemannian Stochastic Recursive Gradient Algorithm with Retraction and Vector Transport and Its Convergence Analysis

1 code implementation ICML 2018 Hiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra

Stochastic variance reduction algorithms have recently become popular for minimizing the average of a large, but finite number of loss functions on a Riemannian manifold.

Low-rank geometric mean metric learning

1 code implementation14 Jun 2018 Mukul Bhutani, Pratik Jawanpuria, Hiroyuki Kasai, Bamdev Mishra

We propose a low-rank approach to learning a Mahalanobis metric from data.

Metric Learning

Stochastic variance reduced multiplicative update for nonnegative matrix factorization

no code implementations30 Oct 2017 Hiroyuki Kasai

Nonnegative matrix factorization (NMF), a dimensionality reduction and factor analysis method, is a special case in which factor matrices have low-rank nonnegative constraints.

Dimensionality Reduction

SGDLibrary: A MATLAB library for stochastic gradient descent algorithms

1 code implementation27 Oct 2017 Hiroyuki Kasai

The purpose of the library is to provide researchers and implementers a comprehensive evaluation environment for the use of these algorithms on various ML problems.

Stochastic Optimization

Fast online low-rank tensor subspace tracking by CP decomposition using recursive least squares from incomplete observations

no code implementations29 Sep 2017 Hiroyuki Kasai

We consider the problem of online subspace tracking of a partially observed high-dimensional data stream corrupted by noise, where we assume that the data lie in a low-dimensional linear subspace.

A Riemannian gossip approach to subspace learning on Grassmann manifold

no code implementations1 May 2017 Bamdev Mishra, Hiroyuki Kasai, Pratik Jawanpuria, Atul Saroop

Interesting applications in this setting include low-rank matrix completion and low-dimensional multivariate regression, among others.

Low-Rank Matrix Completion

Riemannian stochastic variance reduced gradient algorithm with retraction and vector transport

1 code implementation18 Feb 2017 Hiroyuki Sato, Hiroyuki Kasai, Bamdev Mishra

In recent years, stochastic variance reduction algorithms have attracted considerable attention for minimizing the average of a large but finite number of loss functions.

Low-Rank Matrix Completion

State Duration and Interval Modeling in Hidden Semi-Markov Model for Sequential Data Analysis

no code implementations24 Aug 2016 Hiromi Narimatsu, Hiroyuki Kasai

Therefore, we particularly examine the structure of sequential data, and extract the necessity of `state duration' and `state interval' of events for efficient and rich representation of sequential data.

Time Series

Network Volume Anomaly Detection and Identification in Large-scale Networks based on Online Time-structured Traffic Tensor Tracking

no code implementations19 Aug 2016 Hiroyuki Kasai, Wolfgang Kellerer, Martin Kleinsteuber

This problem is cast as a low-rank subspace tracking problem for normal flows under incomplete observations, and an outlier detection problem for abnormal flows.

Anomaly Detection Outlier Detection

Low-rank tensor completion: a Riemannian manifold preconditioning approach

no code implementations26 May 2016 Hiroyuki Kasai, Bamdev Mishra

We propose a novel Riemannian manifold preconditioning approach for the tensor completion problem with rank constraint.

Riemannian optimization

Riemannian stochastic variance reduced gradient on Grassmann manifold

1 code implementation24 May 2016 Hiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra

In this paper, we propose a novel Riemannian extension of the Euclidean stochastic variance reduced gradient algorithm (R-SVRG) to a compact manifold search space.

Low-Rank Matrix Completion Translation

A Riemannian gossip approach to decentralized matrix completion

no code implementations23 May 2016 Bamdev Mishra, Hiroyuki Kasai, Atul Saroop

In this paper, we propose novel gossip algorithms for the low-rank decentralized matrix completion problem.

Matrix Completion

Online Low-Rank Tensor Subspace Tracking from Incomplete Data by CP Decomposition using Recursive Least Squares

1 code implementation23 Feb 2016 Hiroyuki Kasai

We propose an online tensor subspace tracking algorithm based on the CP decomposition exploiting the recursive least squares (RLS), dubbed OnLine Low-rank Subspace tracking by TEnsor CP Decomposition (OLSTEC).

Numerical Analysis

Duration and Interval Hidden Markov Model for Sequential Data Analysis

no code implementations20 Aug 2015 Hiromi Narimatsu, Hiroyuki Kasai

Analysis of sequential event data has been recognized as one of the essential tools in data modeling and analysis field.

Time Series

Riemannian preconditioning for tensor completion

no code implementations6 Jun 2015 Hiroyuki Kasai, Bamdev Mishra

We propose a novel Riemannian preconditioning approach for the tensor completion problem with rank constraint.

Riemannian optimization

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