You need to log in to edit.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

no code implementations • 29 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.

no code implementations • 10 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.

1 code implementation • 1 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.

1 code implementation • 7 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.

2 code implementations • 25 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.

1 code implementation • 25 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.

no code implementations • 28 Oct 2020 • Jianming Huang, Hiroyuki Kasai

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

no code implementations • 25 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 $i = \{1,\ldots, K \}$.

no code implementations • 11 Feb 2019 • Hiroyuki Kasai, Bamdev Mishra

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

1 code implementation • 4 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.

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.

1 code implementation • 3 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.

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.

1 code implementation • 14 Jun 2018 • Mukul Bhutani, Pratik Jawanpuria, Hiroyuki Kasai, Bamdev Mishra

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

no code implementations • 30 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.

1 code implementation • 27 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.

no code implementations • 29 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.

no code implementations • 1 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.

no code implementations • 15 Mar 2017 • Hiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra

The present paper proposes a Riemannian stochastic quasi-Newton algorithm with variance reduction (R-SQN-VR).

1 code implementation • 18 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.

no code implementations • 24 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.

no code implementations • 19 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.

no code implementations • 26 May 2016 • Hiroyuki Kasai, Bamdev Mishra

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

1 code implementation • 24 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.

no code implementations • 23 May 2016 • Bamdev Mishra, Hiroyuki Kasai, Atul Saroop

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

1 code implementation • 23 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

no code implementations • 20 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.

no code implementations • 6 Jun 2015 • Hiroyuki Kasai, Bamdev Mishra

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

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.