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07/01/2023
Safe Screening for Unbalanced Optimal Transport
This paper introduces a framework that utilizes the Safe Screening techn...
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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...
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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...
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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...
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03/10/2021
Fast block-coordinate Frank-Wolfe algorithm for semi-relaxed optimal transport
Optimal transport (OT), which provides a distance between two probabilit...
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03/01/2021
Manifold optimization for optimal transport
Optimal transport (OT) has recently found widespread interest in machine...
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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...
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11/25/2020
Wasserstein k-means with sparse simplex projection
This paper presents a proposal of a faster Wasserstein k-means algorithm...
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11/25/2020
Consistency-aware and Inconsistency-aware Graph-based Multi-view Clustering
Multi-view data analysis has gained increasing popularity because multi-...
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10/28/2020
Graph embedding using multi-layer adjacent point merging model
For graph classification tasks, many traditional kernel methods focus on...
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06/25/2019
Riemannian optimization on the simplex of positive definite matrices
We discuss optimization-related ingredients for the Riemannian manifold ...
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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...
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02/04/2019
Adaptive stochastic gradient algorithms on Riemannian manifolds
Adaptive stochastic gradient algorithms in the Euclidean space have attr...
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10/03/2018
McTorch, a manifold optimization library for deep learning
In this paper, we introduce McTorch, a manifold optimization library for...
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06/14/2018
Low-rank geometric mean metric learning
We propose a low-rank approach to learning a Mahalanobis metric from dat...
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10/30/2017
Stochastic variance reduced multiplicative update for nonnegative matrix factorization
Nonnegative matrix factorization (NMF), a dimensionality reduction and f...
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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...
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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...
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03/15/2017
Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis
Stochastic variance reduction algorithms have recently become popular fo...
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02/18/2017
Riemannian stochastic variance reduced gradient
Stochastic variance reduction algorithms have recently become popular fo...
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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...
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05/26/2016
Low-rank tensor completion: a Riemannian manifold preconditioning approach
We propose a novel Riemannian manifold preconditioning approach for the ...
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05/24/2016
Riemannian stochastic variance reduced gradient on Grassmann manifold
Stochastic variance reduction algorithms have recently become popular fo...
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08/20/2015
