Results 21 to 30 of about 14,028 (258)

Local Connectivity Enhanced Sparse Representation

IEEE Access, 2020
During the past two decades, the subspace clustering problem has attracted much attention. Since the data set in real-world problems usually contains a lot of categories, it seems that the large subspace number (LSN) subspace clustering has great ...
Kewei Tang, Liying Cao, Jinhong Li, Xingxuan Peng, Zhixun Su, Xiyan Sun, Xiaonan Luo   +6 more
doaj   +1 more source

Hyperspectral Image Super-Resolution via Adaptive Factor Group Sparsity Regularization-Based Subspace Representation

Remote Sensing, 2023
Hyperspectral image (HSI) super-resolution is a vital technique that generates high spatial-resolution HSI (HR-HSI) by integrating information from low spatial-resolution HSI with high spatial-resolution multispectral image (MSI).
Yidong Peng, Weisheng Li, Xiaobo Luo, Jiao Du   +3 more
doaj   +1 more source

Subspace-Sparse Representation

CoRR, 2015
15 pages, 3 figures, previous version published in ICML ...
Chong You, René Vidal
openaire   +2 more sources

Identifying Interpretable Subspaces in Image Representations

CoRR, 2023
Published at ICML 2023 Code: https://github.com/NehaKalibhat/falcon ...
Neha Mukund Kalibhat, Shweta Bhardwaj, C. Bayan Bruss, Hamed Firooz, Maziar Sanjabi, Soheil Feizi   +5 more
openaire   +3 more sources

Subspace clustering with dense representations

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
2013 IEEE International Conference on Acoustics, Speech and Signal ...
Dyer, Eva L., Studer, Christoph; id_orcid0000-0001-8950-6267, Baraniuk, Richard G.   +2 more
openaire   +2 more sources

Multi-Subspace Representation and Discovery [PDF]

, 2011
This paper presents the multi-subspace discovery problem and provides a theoretical solution which is guaranteed to recover the number of subspaces, the dimensions of each subspace, and the members of data points of each subspace simultaneously. We further propose a data representation model to handle noisy real world data.
Dijun Luo, Feiping Nie 0001, Chris H. Q. Ding, Heng Huang 0001   +3 more
openaire   +1 more source

Kernel Block Diagonal Representation Subspace Clustering with Similarity Preservation

Applied Sciences, 2023
Subspace clustering methods based on the low-rank and sparse model are effective strategies for high-dimensional data clustering. However, most existing low-rank and sparse methods with self-expression can only deal with linear structure data effectively,
Yifang Yang, Fei Li
doaj   +1 more source

Dimension reduction graph‐based sparse subspace clustering for intelligent fault identification of rolling element bearings

International Journal of Mechanical System Dynamics, 2021
Sparse subspace clustering (SSC) is a spectral clustering methodology. Since high‐dimensional data are often dispersed over the union of many low‐dimensional subspaces, their representation in a suitable dictionary is sparse.
Le Zhao, Shaopu Yang, Yongqiang Liu
doaj   +1 more source

Discriminant Subspace Low-Rank Representation Algorithm for Electroencephalography-Based Alzheimer’s Disease Recognition

Frontiers in Aging Neuroscience, 2022
Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that often occurs in the elderly. Electroencephalography (EEG) signals have a strong correlation with neuropsychological test results and brain structural changes.
Tusheng Tang, Hui Li, Guohua Zhou, Guohua Zhou, Xiaoqing Gu, Jing Xue   +5 more
doaj   +1 more source

An Efficient Representation-Based Subspace Clustering Framework for Polarized Hyperspectral Images

Remote Sensing, 2019
Recently, representation-based subspace clustering algorithms for hyperspectral images (HSIs) have been developed with the assumption that pixels belonging to the same land-cover class lie in the same subspace. Polarization is regarded to be a complement
Zhengyi Chen, Chunmin Zhang, Tingkui Mu, Tingyu Yan, Zeyu Chen, Yanqiang Wang   +5 more
doaj   +1 more source