no code implementations • 14 Mar 2022 • Yuning Qiu, Guoxu Zhou, Qibin Zhao, Shengli Xie
Experimental results on both synthetic and real-world data demonstrate the effectiveness and efficiency of the proposed model in recovering noisy incomplete tensor data compared with state-of-the-art tensor completion models.
no code implementations • 3 Jan 2022 • Yuyuan Yu, Guoxu Zhou, Haonan Huang, Shengli Xie, Qibin Zhao
However, existing strategies cannot take advantage of semi-supervised information, only distinguishing the importance of views from a data feature perspective, which is often influenced by low-quality views then leading to poor performance.
no code implementations • 19 Oct 2021 • Xumin Huang, Peichun Li, Rong Yu, Yuan Wu, Kan Xie, Shengli Xie
In PVEC, different PLOs recruit PVs as edge computing nodes for offloading services through an incentive mechanism, which is designed according to the computation demand and parking capacity constraints derived from FedParking.
no code implementations • 21 Jan 2021 • Ci Chen, Lihua Xie, Yi Jiang, Kan Xie, Shengli Xie
To remove such a requirement, an off-policy reinforcement learning algorithm is proposed using only the measured output data along the trajectories of the system and the reference output.
Dynamical Systems
no code implementations • 12 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.
no code implementations • 14 Oct 2019 • Jinshi Yu, Weijun Sun, Yuning Qiu, Shengli Xie
In tensor completion, the latent nuclear norm is commonly used to induce low-rank structure, while substantially failing to capture the global information due to the utilization of unbalanced unfolding scheme.
no code implementations • 10 Oct 2018 • Jiawei Wang, Zhaoshui He, Chengjian Feng, Zhouping Zhu, Qinzhuang Lin, Jun Lv, Shengli Xie
Data collection and annotation are time-consuming in machine learning, expecially for large scale problem.
no code implementations • 22 May 2018 • Chao Li, Mohammad Emtiyaz Khan, Zhun Sun, Gang Niu, Bo Han, Shengli Xie, Qibin Zhao
Exact recovery of tensor decomposition (TD) methods is a desirable property in both unsupervised learning and scientific data analysis.
no code implementations • 20 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.
no code implementations • 30 Aug 2016 • Ming Yin, Junbin Gao, Shengli Xie, Yi Guo
Multi-view subspace clustering is based on the fact that the multi-view data are generated from a latent subspace.
1 code implementation • 17 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.
no code implementations • 27 Jan 2016 • Ming Yin, Shengli Xie, Yi Guo, Junbin Gao, Yun Zhang
Due to its promising classification performance, sparse representation based classification(SRC) algorithm has attracted great attention in the past few years.
no code implementations • CVPR 2016 • Ming Yin, Yi Guo, Junbin Gao, Zhaoshui He, Shengli Xie
Sparse subspace clustering (SSC), as one of the most successful subspace clustering methods, has achieved notable clustering accuracy in computer vision tasks.
no code implementations • 29 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.
no code implementations • 17 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.
no code implementations • 15 Nov 2012 • Guoxu Zhou, Andrzej Cichocki, Shengli Xie
Canonical Polyadic (or CANDECOMP/PARAFAC, CP) decompositions (CPD) are widely applied to analyze high order tensors.