Results 51 to 60 of about 49,338 (223)
Subspace clustering of dimensionality-reduced data
, 2014 Subspace clustering refers to the problem of clustering unlabeled high-dimensional data points into a union of low-dimensional linear subspaces, assumed unknown.
Bölcskei, Helmut +2 more
core +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
Staged Diversity‐Constrained Machine Learning for High‐Dimensional Reaction Condition Optimization
Angewandte Chemie, EarlyView.Staged diversity‐constrained modeling enables efficient navigation of high‐dimensional reaction spaces, validated on cross‐coupling HTE data and applied to ruthenium‐catalyzed meta‐C─H functionalization. ABSTRACT Optimizing reaction conditions in high‐dimensional chemical spaces remains a central challenge in modern synthesis.
Shu‐Wen Li +5 more
wiley +2 more sources
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
Subspace clustering using ensembles of K-subspaces
Information and Inference: A Journal of the IMA, 2020 Abstract Subspace clustering is the unsupervised grouping of points lying near a union of low-dimensional linear subspaces. Algorithms based directly on geometric properties of such data tend to either provide poor empirical performance, lack theoretical guarantees or depend heavily on their initialization.
Lipor, John +3 more
openaire +2 more sources
Electric‐Current‐Assisted Nucleation of Zero‐Field Hopfion Rings
Advanced Materials, EarlyView.This work reports a novel and efficient nucleation protocol for 3D localized topological magnetic solitons‐hopfion rings in chiral magnets using pulsed electric currents. By using Lorentz transmission electron microscopy and topological analysis, we report characteristic features and extraordinary stability of hopfion rings in zero or inverted external
Xiaowen Chen +12 more
wiley +1 more source
Multi-view clustering via simultaneously learning shared subspace and affinity matrix
International Journal of Advanced Robotic Systems, 2017 Due to the existence of multiple views in many real-world data sets, multi-view clustering is increasingly popular. Many approaches have been investigated, among which the subspace clustering methods finding the underlying subspaces of data have been ...
Nan Xu +4 more
doaj +1 more source
Deep Subspace Clustering with Block Diagonal Constraint
Applied Sciences, 2020 The deep subspace clustering method, which adopts deep neural networks to learn a representation matrix for subspace clustering, has shown good performance.
Jing Liu, Yanfeng Sun, Yongli Hu
doaj +1 more source
Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering
, 2016 State-of-the-art subspace clustering methods are based on expressing each data point as a linear combination of other data points while regularizing the matrix of coefficients with $\ell_1$, $\ell_2$ or nuclear norms.
Li, Chun-Guang +3 more
core +1 more source
Sparse subspace clustering [PDF]
2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009 We propose a method based on sparse representation (SR) to cluster data drawn from multiple low-dimensional linear or affine subspaces embedded in a high-dimensional space. Our method is based on the fact that each point in a union of subspaces has a SR with respect to a dictionary formed by all other data points.
Ehsan Elhamifar, Rene Vidal
openaire +1 more source