Results 41 to 50 of about 49,736 (243)

Considering spatiotemporal evolutionary information in dynamic multi‐objective optimisation

CAAI Transactions on Intelligence Technology, EarlyView., 2023
Abstract Preserving population diversity and providing knowledge, which are two core tasks in the dynamic multi‐objective optimisation (DMO), are challenging since the sampling space is time‐ and space‐varying. Therefore, the spatiotemporal property of evolutionary information needs to be considered in the DMO.
Qinqin Fan, Min Jiang, Wentao Huang, Qingchao Jiang   +3 more
wiley   +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

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

Neighborhood Selection for Thresholding-based Subspace Clustering

, 2014
Subspace clustering refers to the problem of clustering high-dimensional data points into a union of low-dimensional linear subspaces, where the number of subspaces, their dimensions and orientations are all unknown. In this paper, we propose a variation
Agustsson, Eirikur, Bölcskei, Helmut, Heckel, Reinhard   +2 more
core   +1 more source

Subspace Clustering through Sub-Clusters

, 2018
The problem of dimension reduction is of increasing importance in modern data analysis. In this paper, we consider modeling the collection of points in a high dimensional space as a union of low dimensional subspaces. In particular we propose a highly scalable sampling based algorithm that clusters the entire data via first spectral clustering of a ...
Li, Weiwei, Hannig, Jan, Mukherjee, Sayan   +2 more
openaire   +3 more sources

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

Dynamic Ensemble Learning With Multi-View Kernel Collaborative Subspace Clustering for Hyperspectral Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Recently, a series of collaborative representation (CR) methods have attracted much attention for hyperspectral images classification. In this article, two CR-based dynamic ensemble selection (DES) methods using multiview kernel collaborative subspace ...
Hongliang Lu, Hongjun Su, Jun Hu, Qian Du   +3 more
doaj   +1 more source

Low-rank sparse subspace clustering with a clean dictionary

Journal of Algorithms & Computational Technology, 2021
Low-Rank Representation (LRR) and Sparse Subspace Clustering (SSC) are considered as the hot topics of subspace clustering algorithms. SSC induces the sparsity through minimizing the l 1 -norm of the data matrix while LRR promotes a low-rank structure ...
Cong-Zhe You, Zhen-Qiu Shu, Hong-Hui Fan
doaj   +1 more source

Unified Low-Rank Subspace Clustering with Dynamic Hypergraph for Hyperspectral Image

Remote Sensing, 2021
Low-rank representation with hypergraph regularization has achieved great success in hyperspectral imagery, which can explore global structure, and further incorporate local information.
Jinhuan Xu, Liang Xiao, Jingxiang Yang
doaj   +1 more source