Results 61 to 70 of about 1,461,923 (295)
Frontiers in Energy Research, 2022 Power system clustering is an effective method for realizing voltage control and preventing failure propagation. Various approaches are used for power system clustering. Graph-theory-based spectral clustering methods are widely used because they follow a
Juhwan Kim +5 more
doaj +1 more source
Mini-batch spectral clustering [PDF]
2017 International Joint Conference on Neural Networks (IJCNN), 2017 The cost of computing the spectrum of Laplacian matrices hinders the application of spectral clustering to large data sets. While approximations recover computational tractability, they can potentially affect clustering performance. This paper proposes a practical approach to learn spectral clustering based on adaptive stochastic gradient optimization.
Yufei Han 0001 +2 more
openaire +3 more sources
Multi-agent Behaviour Segmentation via Spectral Clustering [PDF]
, 2007 18.02.14 KB. OK to add accepted version to spiral, AAAI permits.We examine the application of spectral clustering for breaking up the behaviour of a multi-agent system in space and time into smaller, independent elements.
Takács, B, Demiris, Y, Butler, S
core
Toward Efficient and Incremental Spectral Clustering via Parametric Spectral Clustering [PDF]
, 2023 Spectral clustering is a popular method for effectively clustering nonlinearly separable data. However, computational limitations, memory requirements, and the inability to perform incremental learning challenge its widespread application.
Chen, Hung-Hsuan, Chen, Jo-Chun
core
Design and analysis strategies for robust microbiome ageing research
FEBS Letters, EarlyView.The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik +5 more
wiley +1 more source
Applications in Plant Sciences, 2020 Premise Statistical methods used by most morphologists to validate species boundaries (such as principal component analysis [PCA] and non‐metric multidimensional scaling [nMDS]) are limiting because these methods are mostly used as visualization methods,
Preeti Saryan +2 more
doaj +1 more source
FEBS Letters, EarlyView.Obesity raises blood levels of PAI‐1, a protein linked to metabolic dysfunction‐associated steatotic liver disease in people with obesity. In female mice fed a high‐fat diet, partially lowering PAI‐1 led to smaller subcutaneous fat cells and lower liver cholesterol, without changing body weight or insulin sensitivity.
Claudia E. Ramirez Bustamante +10 more
wiley +1 more source
Analysis of the singular value decomposition as a tool for processing microarray expression data [PDF]
, 2005 We give two informative derivations of a spectral algorithm for clustering and partitioning a bi-partite graph. In the first case we begin with a discrete optimization problem that relaxes into a tractable continuous analogue.
Kalna, G. +5 more
core +3 more sources
Kernel Spectral Clustering and Applications [PDF]
, 2016 In this chapter we review the main literature related to kernel spectral clustering (KSC), an approach to clustering cast within a kernel-based optimization setting. KSC represents a least-squares support vector machine based formulation of spectral clustering described by a weighted kernel PCA objective.
Langone, Rocco +3 more
openaire +3 more sources
Spectral Clustering with Smooth Tiny Clusters
CoRR, 2020 Spectral clustering is one of the most prominent clustering approaches. The distance-based similarity is the most widely used method for spectral clustering. However, people have already noticed that this is not suitable for multi-scale data, as the distance varies a lot for clusters with different densities.
Hengrui Wang +3 more
openaire +2 more sources