Results 71 to 80 of about 49,338 (223)
Convolutional Subspace Clustering Network With Block Diagonal Prior
IEEE Access, 2020 Standard methods of subspace clustering are based on self-expressiveness in the original data space, which states that a data point in a subspace can be expressed as a linear combination of other points. However, the real data in raw form are usually not
Junjian Zhang +4 more
doaj +1 more source
A Survey on Soft Subspace Clustering
, 2016 Subspace clustering (SC) is a promising clustering technology to identify clusters based on their associations with subspaces in high dimensional spaces. SC can be classified into hard subspace clustering (HSC) and soft subspace clustering (SSC).
Choi, Kup-Sze +4 more
core +1 more source
The Annals of Statistics, 2014 Subspace clustering refers to the task of finding a multi-subspace representation that best fits a collection of points taken from a high-dimensional space. This paper introduces an algorithm inspired by sparse subspace clustering (SSC) [In IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2009) 2790-2797] to cluster noisy data, and ...
Soltanolkotabi, Mahdi +2 more
openaire +3 more sources
Discriminative Subspace Clustering [PDF]
2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013 We present a novel method for clustering data drawn from a union of arbitrary dimensional subspaces, called Discriminative Subspace Clustering (DiSC). DiSC solves the subspace clustering problem by using a quadratic classifier trained from unlabeled data (clustering by classification).
Vasileios Zografos +2 more
openaire +1 more source
Advanced Intelligent Discovery, EarlyView.This work introduces a novel framework for identifying non‐small cell lung cancer biomarkers from hundreds of volatile organic compounds in breath, analyzed via gas chromatography‐mass spectrometry. This method integrates generative data augmentation and multi‐view feature selection, providing a stable and accurate solution for biomarker discovery in ...
Guancheng Ren +10 more
wiley +1 more source
Fusion and Enhancement of Consensus Matrix for Multi-View Subspace Clustering
Mathematics, 2023 Multi-view subspace clustering is an effective method that has been successfully applied to many applications and has attracted the attention of scholars.
Xiuqin Deng, Yifei Zhang, Fangqing Gu
doaj +1 more source
Advanced Intelligent Discovery, EarlyView.This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley +1 more source
Impact Parameter Analysis of Subspace Clustering
International Journal of Distributed Sensor Networks, 2015 Subspace clustering, which detects all clusters in affine subspaces of a given high dimensional vector space, is used in various applications, including e-business.
Dongjin Lee, Junho Shim
doaj +1 more source
Advanced Intelligent Systems, EarlyView.This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He +2 more
wiley +1 more source
Noisy Sparse Subspace Clustering
, 2013 Manuscript currently under review at journal of machine learning research.
Wang, Yu-Xiang, Xu, Huan
openaire +3 more sources