Search Results for author: Guoxu Zhou

Found 14 papers, 0 papers with code

Fast Hypergraph Regularized Nonnegative Tensor Ring Factorization Based on Low-Rank Approximation

no code implementations6 Sep 2021 Xinhai Zhao, Yuyuan Yu, Guoxu Zhou, Qibin Zhao, Weijun Sun

For the high dimensional data representation, nonnegative tensor ring (NTR) decomposition equipped with manifold learning has become a promising model to exploit the multi-dimensional structure and extract the feature from tensor data.

Improving EEG Decoding via Clustering-based Multi-task Feature Learning

no code implementations12 Dec 2020 Yu Zhang, Tao Zhou, Wei Wu, Hua Xie, Hongru Zhu, Guoxu Zhou, Andrzej Cichocki

With the encoded label matrix, we devise a novel multi-task learning algorithm by exploiting the subclass relationship to jointly optimize the EEG pattern features from the uncovered subclasses.

EEG Eeg Decoding +1

Graph Regularized Nonnegative Tensor Ring Decomposition for Multiway Representation Learning

no code implementations12 Oct 2020 Yuyuan Yu, Guoxu Zhou, Ning Zheng, Shengli Xie, Qibin Zhao

Tensor ring (TR) decomposition is a powerful tool for exploiting the low-rank nature of multiway data and has demonstrated great potential in a variety of important applications.

Representation Learning


no code implementations ICLR 2019 Xinqi Chen, Ming Hou, Guoxu Zhou, Qibin Zhao

Recent deep multi-task learning (MTL) has been witnessed its success in alleviating data scarcity of some task by utilizing domain-specific knowledge from related tasks.

Multi-Task Learning

Tensor-Ring Nuclear Norm Minimization and Application for Visual Data Completion

no code implementations21 Mar 2019 Jinshi Yu, Chao Li, Qibin Zhao, Guoxu Zhou

Tensor ring (TR) decomposition has been successfully used to obtain the state-of-the-art performance in the visual data completion problem.

Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization

no code implementations20 Mar 2018 Jinshi Yu, Guoxu Zhou, Andrzej Cichocki, Shengli Xie

Nonsmooth Nonnegative Matrix Factorization (nsNMF) is capable of producing more localized, less overlapped feature representations than other variants of NMF while keeping satisfactory fit to data.

Deep Approximately Orthogonal Nonnegative Matrix Factorization for Clustering

no code implementations20 Nov 2017 Yuning Qiu, Guoxu Zhou, Kan Xie

Nonnegative Matrix Factorization (NMF) is a widely used technique for data representation.

Tensor Ring Decomposition

no code implementations17 Jun 2016 Qibin Zhao, Guoxu Zhou, Shengli Xie, Liqing Zhang, Andrzej Cichocki

In this paper, we introduce a fundamental tensor decomposition model to represent a large dimensional tensor by a circular multilinear products over a sequence of low dimensional cores, which can be graphically interpreted as a cyclic interconnection of 3rd-order tensors, and thus termed as tensor ring (TR) decomposition.

Tensor Decomposition Tensor Networks

Linked Component Analysis from Matrices to High Order Tensors: Applications to Biomedical Data

no code implementations29 Aug 2015 Guoxu Zhou, Qibin Zhao, Yu Zhang, Tülay Adalı, Shengli Xie, Andrzej Cichocki

With the increasing availability of various sensor technologies, we now have access to large amounts of multi-block (also called multi-set, multi-relational, or multi-view) data that need to be jointly analyzed to explore their latent connections.

Tensor Decomposition

Bayesian Robust Tensor Factorization for Incomplete Multiway Data

no code implementations9 Oct 2014 Qibin Zhao, Guoxu Zhou, Liqing Zhang, Andrzej Cichocki, Shun-ichi Amari

We propose a generative model for robust tensor factorization in the presence of both missing data and outliers.

Model Selection Variational Inference

Efficient Nonnegative Tucker Decompositions: Algorithms and Uniqueness

no code implementations17 Apr 2014 Guoxu Zhou, Andrzej Cichocki, Qibin Zhao, Shengli Xie

Nonnegative Tucker decomposition (NTD) is a powerful tool for the extraction of nonnegative parts-based and physically meaningful latent components from high-dimensional tensor data while preserving the natural multilinear structure of data.

Frequency Recognition in SSVEP-based BCI using Multiset Canonical Correlation Analysis

no code implementations26 Aug 2013 Yu Zhang, Guoxu Zhou, Jing Jin, Xingyu Wang, Andrzej Cichocki

Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs).


Accelerated Canonical Polyadic Decomposition by Using Mode Reduction

no code implementations15 Nov 2012 Guoxu Zhou, Andrzej Cichocki, Shengli Xie

Canonical Polyadic (or CANDECOMP/PARAFAC, CP) decompositions (CPD) are widely applied to analyze high order tensors.

Dimensionality Reduction

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