Results 41 to 50 of about 9,565 (204)

Linearity-Aware Subspace Clustering

, 2022
Obtaining a good similarity matrix is extremely important in subspace clustering. Current state-of-the-art methods learn the similarity matrix through self-expressive strategy.
Li, Jun, Xu, Yesong, Chen, Shuo, Qian, Jianjun   +3 more
core   +1 more source

Directional clustering through matrix factorization [PDF]

, 2016
This paper deals with a clustering problem where feature vectors are clustered depending on the angle between feature vectors, that is, feature vectors are grouped together if they point roughly in the same direction.
Blumensath, Thomas
core   +1 more source

Dimension Selected Subspace Clustering

, 2021
Subspace clustering is a popular method for clustering unlabelled data. However, the computational cost of the subspace clustering algorithm can be unaffordable when dealing with a large data set.
Chambers, Jonathon, Li, Shuoyang, Shuoyang Li, Wang, Wenwu, Wenwu Wang, Luo, Yuhui, Yuhui Luo, Jonathon Chambers   +7 more
core   +1 more source

K-Subspace Clustering [PDF]

, 2009
The widely used K-means clustering deals with ball-shaped (spherical Gaussian) clusters. In this paper, we extend the K-means clustering to accommodate extended clusters in subspaces, such as line-shaped clusters, plane-shaped clusters, and ball-shaped clusters. The algorithm retains much of the K-means clustering flavors: easy to implement and fast to
Dingding Wang 0001, Chris H. Q. Ding, Tao Li 0001   +2 more
openaire   +1 more source

Unsupervised Locality-Preserving Robust Latent Low-Rank Recovery-Based Subspace Clustering for Fault Diagnosis

IEEE Access, 2018
With the increasing demand for unsupervised learning for fault diagnosis, the subspace clustering has been considered as a promising technique enabling unsupervised fault diagnosis. Although various subspace clustering methods have been developed to deal
Jie Gao, Myeongsu Kang, Jing Tian, Lifeng Wu, Michael Pecht   +4 more
doaj   +1 more source

Soft Subspace Clustering Algorithm Optimized by Brain Storm Algorithm for Breast MR Image

Jisuanji kexue yu tansuo, 2020
The traditional soft subspace clustering algorithm is very susceptible to the initial clustering center and noise data when segmenting breast MR images with large amount of information, uneven intensity and boundary blur, which results in that algorithm ...
FAN Hong, SHI Xiaomin, YAO Ruoxia
doaj   +1 more source

Unsupervised radar signal recognition based on multi‐block – Multi‐view Low‐Rank Sparse Subspace Clustering

IET Radar, Sonar & Navigation, 2022
In the field of radar reconnaissance, unsupervised recognition of radar signals is a particularly important method for classifying different signals and estimating signal parameters.
Lutao Liu, Shuai Xu
doaj   +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

Multi-view subspace clustering

, 2016
For many computer vision applications, the data sets distribute on certain low;dimensional subspaces. Subspace clustering is to find such underlying subspaces and cluster the data points correctly.
Gao, Hongchang (hongchanggao@gmail.com), Nie, Feiping, Li, Xuelong, Huang, Heng, Gao, Hongchang   +4 more
core   +1 more source

Greedy Subspace Clustering

CoRR, 2014
We consider the problem of subspace clustering: given points that lie on or near the union of many low-dimensional linear subspaces, recover the subspaces. To this end, one first identifies sets of points close to the same subspace and uses the sets to estimate the subspaces.
Dohyung Park, Constantine Caramanis, Sujay Sanghavi   +2 more
openaire   +3 more sources