Hiroyuki Kasai | DeepAI

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Hiroyuki Sato
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Jianming Huang
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Takumi Fukunaga
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∙ 07/01/2023

Safe Screening for Unbalanced Optimal Transport

This paper introduces a framework that utilizes the Safe Screening techn...

0 Xun Su, et al. ∙

∙ 07/09/2022

Wasserstein Graph Distance based on L_1-Approximated Tree Edit Distance between Weisfeiler-Lehman Subtrees

The Weisfeiler-Lehman (WL) test has been widely applied to graph kernels...

0 Zhongxi Fang, et al. ∙

∙ 05/27/2022

On the Convergence of Semi-Relaxed Sinkhorn with Marginal Constraint and OT Distance Gaps

This paper presents consideration of the Semi-Relaxed Sinkhorn (SR-Sinkh...

0 Takumi Fukunaga, et al. ∙

∙ 05/27/2022

Block-coordinate Frank-Wolfe algorithm and convergence analysis for semi-relaxed optimal transport problem

The optimal transport (OT) problem has been used widely for machine lear...

0 Takumi Fukunaga, et al. ∙

∙ 03/10/2021

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

Optimal transport (OT), which provides a distance between two probabilit...

14 Takumi Fukunaga, et al. ∙

∙ 03/01/2021

Manifold optimization for optimal transport

Optimal transport (OT) has recently found widespread interest in machine...

0 Bamdev Mishra, et al. ∙

∙ 12/07/2020

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

For graph classification tasks, many methods use a common strategy to ag...

0 Jianming Huang, et al. ∙

∙ 11/25/2020

Wasserstein k-means with sparse simplex projection

This paper presents a proposal of a faster Wasserstein k-means algorithm...

0 Takumi Fukunaga, et al. ∙

∙ 11/25/2020

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

Multi-view data analysis has gained increasing popularity because multi-...

0 Mitsuhiko Horie, et al. ∙

∙ 10/28/2020

Graph embedding using multi-layer adjacent point merging model

For graph classification tasks, many traditional kernel methods focus on...

0 Jianming Huang, et al. ∙

∙ 06/25/2019

Riemannian optimization on the simplex of positive definite matrices

We discuss optimization-related ingredients for the Riemannian manifold ...

0 Bamdev Mishra, et al. ∙

∙ 02/11/2019

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

Dictionary leaning (DL) and dimensionality reduction (DR) are powerful t...

0 Hiroyuki Kasai, et al. ∙

∙ 02/04/2019

Adaptive stochastic gradient algorithms on Riemannian manifolds

Adaptive stochastic gradient algorithms in the Euclidean space have attr...

0 Hiroyuki Kasai, et al. ∙

∙ 10/03/2018

McTorch, a manifold optimization library for deep learning

In this paper, we introduce McTorch, a manifold optimization library for...

0 Mayank Meghwanshi, et al. ∙

∙ 06/14/2018

Low-rank geometric mean metric learning

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

0 Mukul Bhutani, et al. ∙

∙ 10/30/2017

Stochastic variance reduced multiplicative update for nonnegative matrix factorization

Nonnegative matrix factorization (NMF), a dimensionality reduction and f...

0 Hiroyuki Kasai, et al. ∙

∙ 10/27/2017

SGDLibrary: A MATLAB library for stochastic gradient descent algorithms

We consider the problem of finding the minimizer of a function f: R^d →R...

0 Hiroyuki Kasai, et al. ∙

∙ 09/29/2017

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

We consider the problem of online subspace tracking of a partially obser...

0 Hiroyuki Kasai, et al. ∙

∙ 03/15/2017

Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis

Stochastic variance reduction algorithms have recently become popular fo...

0 Hiroyuki Kasai, et al. ∙

∙ 02/18/2017

Riemannian stochastic variance reduced gradient

Stochastic variance reduction algorithms have recently become popular fo...

0 Hiroyuki Sato, et al. ∙

∙ 08/19/2016

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

This paper addresses network anomography, that is, the problem of inferr...

0 Hiroyuki Kasai, et al. ∙

∙ 05/26/2016

Low-rank tensor completion: a Riemannian manifold preconditioning approach

We propose a novel Riemannian manifold preconditioning approach for the ...

0 Hiroyuki Kasai, et al. ∙

∙ 05/24/2016

Riemannian stochastic variance reduced gradient on Grassmann manifold

Stochastic variance reduction algorithms have recently become popular fo...

0 Hiroyuki Kasai, et al. ∙

∙ 08/20/2015

Duration and Interval Hidden Markov Model for Sequential Data Analysis

Analysis of sequential event data has been recognized as one of the esse...

0 Hiromi Narimatsu, et al. ∙

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