Robust auto-weighted multi-view subspace clustering with common subspace representation matrix. [PDF]
PLoS ONE, 2017 In many computer vision and machine learning applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is a powerful technology to find the underlying subspaces and cluster data points correctly.
Wenzhang Zhuge +5 more
doaj +2 more sources
Removal of Mixed Noise in Hyperspectral Images Based on Subspace Representation and Nonlocal Low-Rank Tensor Decomposition [PDF]
SensorsHyperspectral images (HSIs) contain abundant spectral and spatial structural information, but they are inevitably contaminated by a variety of noises during data reception and transmission, leading to image quality degradation and subsequent application ...
Chun He, Youhua Wei, Ke Guo, Hongwei Han
doaj +2 more sources
Tensorized Multi-view Subspace Representation Learning [PDF]
International Journal of Computer Vision, 2020 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Changqing Zhang, Huazhu Fu, Wen Li
exaly +2 more sources
Intrinsic Metric Learning With Subspace Representation [PDF]
IEEE Access, 2019 The accuracy of classification and retrieval significantly depends on the metric used to compute the similarity between samples. For preserving the geometric structure, the symmetric positive definite (SPD) manifold is introduced into the metric learning
Lipeng Cai +4 more
doaj +2 more sources
Subspace Clustering by Block Diagonal Representation [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019 This paper studies the subspace clustering problem. Given some data points approximately drawn from a union of subspaces, the goal is to group these data points into their underlying subspaces. Many subspace clustering methods have been proposed and among which sparse subspace clustering and low-rank representation are two representative ones.
Canyi Lu, Jiashi Feng, Zhouchen Lin
exaly +4 more sources
Structure-Constrained Symmetric Low-Rank Representation Algorithm for Subspace Clustering [PDF]
Jisuanji gongcheng, 2021 The potential subspace structure of high-dimensional data can be obtained by using subspace clustering,but the existing methods can not reveal the characteristics of global low-rank structure and local sparse structure of data at the same time,which ...
TAO Yang, BAO Linglang, HU Hao
doaj +1 more source
Dimensionality Reduction Method Combining Representation Learning and Embedded Subspace Learning [PDF]
Jisuanji gongcheng, 2021 When reducing the dimension of sample data, the subspace learning model cannot reveal the data structure or process new samples apart from the training samples.This paper proposes a dimension reduction method that fuses representation learning and ...
TAO Yang, BAO Linglang, HU Hao
doaj +1 more source
Fusing Local and Global Information for One-Step Multi-View Subspace Clustering
Applied Sciences, 2022 Multi-view subspace clustering has drawn significant attention in the pattern recognition and machine learning research community. However, most of the existing multi-view subspace clustering methods are still limited in two aspects.
Yiqiang Duan +3 more
doaj +1 more source
On Minimal Subspaces in Tensor Representations [PDF]
Foundations of Computational Mathematics, 2012 Agraïments: This work is partially supported by the PRCEU-UCH30/10 grant of the Universidad CEU Cardenal Herrera.
Antonio Falcó, Wolfgang Hackbusch
openaire +3 more sources
Scaled Simplex Representation for Subspace Clustering [PDF]
IEEE Transactions on Cybernetics, 2021 Accepted by IEEE Transactions on Cybernetics. 13 pages, 9 figures, 10 tables.
Jun Xu 0019 +6 more
openaire +4 more sources