Results 51 to 60 of about 49,736 (243)
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
, 2015 The Shape Interaction Matrix (SIM) is one of the earliest approaches to performing subspace clustering (i.e., separating points drawn from a union of subspaces).
Ji, Pan, Li, Hongdong, Salzmann, Mathieu
core +1 more source
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
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
Imaging of Biphoton States: Fundamentals and Applications
Advanced Functional Materials, EarlyView.Quantum states of two photons exhibit a rich polarization and spatial structure, which provides a fundamental resource of strongly correlated and entangled states. This review analyzes the physics of these intriguing properties and explores the various techniques and technologies available to measure them, including the state of the art of their ...
Alessio D'Errico, Ebrahim Karimi
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
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
Algorithmic Design of Disordered Networks With Arbitrary Coordination: Application to Biophotonics
Advanced Functional Materials, EarlyView.Predictive Design of Disordered Networks: Disordered network‐like morphologies are abundant in nature, from cytoskeletal networks to bone structures and chalcogenide glasses. These structures are naturally hard to characterize. A new algorithmic tool extends the established Wooten–Weaire–Winer (WWW) algorithm to valencies above 4.
Florin Hemmann +3 more
wiley +1 more source
Ordered Subspace Clustering for Complex Non-Rigid Motion by 3D Reconstruction
Applied Sciences, 2019 As a fundamental and challenging problem, non-rigid structure-from-motion (NRSfM) has attracted a large amount of research interest. It is worth mentioning that NRSfM has been applied to dynamic scene understanding and motion segmentation.
Weinan Du +4 more
doaj +1 more source