Results 51 to 60 of about 64,517 (265)
Spectral clustering with epidemic diffusion [PDF]
Physical Review E, 2013 6 pages, to appear in Physical Review ...
Laura M. Smith +4 more
openaire +3 more sources
Reciprocal control of viral infection and phosphoinositide dynamics
FEBS Letters, EarlyView.Phosphoinositides, although scarce, regulate key cellular processes, including membrane dynamics and signaling. Viruses exploit these lipids to support their entry, replication, assembly, and egress. The central role of phosphoinositides in infection highlights phosphoinositide metabolism as a promising antiviral target.
Marie Déborah Bancilhon, Bruno Mesmin
wiley +1 more source
CoRRCoresets have become an invaluable tool for solving $k$-means and kernel $k$-means clustering problems on large datasets with small numbers of clusters. On the other hand, spectral clustering works well on sparse graphs and has recently been extended to scale efficiently to large numbers of clusters.
Ben Jourdan +3 more
openaire +3 more sources
Compressive Spectral Clustering
CoRR, 2016 Spectral clustering has become a popular technique due to its high performance in many contexts. It comprises three main steps: create a similarity graph between N objects to cluster, compute the first k eigenvectors of its Laplacian matrix to define a feature vector for each object, and run k-means on these features to separate objects into k classes.
Tremblay, Nicolas +3 more
openaire +5 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
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, Maurizio Filippone
openaire +2 more sources
Diversity and complexity in neural organoids
FEBS Letters, EarlyView.Neural organoid research aims to expand genetic diversity on one side and increase tissue complexity on the other. Chimeroids integrate multiple donor genomes within single organoids. Self‐organising multi‐identity organoids, exogenous cell seeding, or enforced assembly of region‐specific organoids contribute to tissue complexity.
Ilaria Chiaradia, Madeline A. Lancaster
wiley +1 more source
Remote Sensing, 2016 Remote sensing image clustering is a challenging task considering its intrinsic complexity. Recently, by combining the spectral and spatial information of the remote sensing data, the clustering performance can be dramatically enhanced, termed as ...
Ailong Ma, Yanfei Zhong, Liangpei Zhang
doaj +1 more source
A Short Text Clustering Algorithm Based on Spectral Cut [PDF]
Jisuanji gongcheng, 2016 Short text has the characteristics of sparsity and high dimension,and the existing clustering algorithm for the large-scale short text has low accuracy and efficiency.Aiming at this problem,a novel clustering method based on spectral clustering theory ...
LI Xiaohong,XIE Meng,MA Huifang,HE Tingnian
doaj +1 more source
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 +2 more sources