Results 61 to 70 of about 541,400 (289)

Correlation Clustering [PDF]

, 2008
Knowledge Discovery in Databases (KDD) is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data.
Zimek, Arthur
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

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

Low rank subspace clustering (LRSC) [PDF]

, 2014
We consider the problem of fitting a union of subspaces to a collection of data points drawn from one or more subspaces and corrupted by noise and/or gross errors.
Favaro, Paolo, Vidal, René
core   +2 more sources

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Subspace Clustering through Sub-Clusters

CoRR, 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 ...
Weiwei Li, Jan Hannig, Sayan Mukherjee 0001   +2 more
openaire   +5 more sources

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

Latent Diffusion Process With Mechanistic Guidance For Designing Functionally Graded Metamaterials With Perfect Connectivity

Advanced Science, EarlyView.
A latent diffusion‐based framework is proposed for designing functionally graded metamaterials with perfect connectivity. By integrating vector‐quantized latent representations with mechanistic guidance, the framework enables accurate inverse design toward target elastic properties.
Jongbin Yu, Dosung Lee, Namjung Kim
wiley   +1 more source

Deep Consistent-Inherent Learning for Cross-Modal Subspace Clustering

Guidance, Navigation and Control
Deep cross-modal clustering has been developing at a rapid pace and attracted great attention. It aims to pursue a consistent subspace from different modalities by conventional neural network and achieve remarkable clustering performance.
Yuzhuo Feng, Demin Zhou
doaj   +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