Results 41 to 50 of about 3,374,256 (360)
KNN-SC: Novel Spectral Clustering Algorithm Using k-Nearest Neighbors
IEEE Access, 2021 Spectral clustering is a well-known graph-theoretic clustering algorithm. Although spectral clustering has several desirable advantages (such as the capability of discovering non-convex clusters and applicability to any data type), it often leads to ...
Jeong-Hun Kim +4 more
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Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs [PDF]
IEEE Robotics and Automation Letters, 2019 We present a novel approach for traffic forecasting in urban traffic scenarios using a combination of spectral graph analysis and deep learning. We predict both the low-level information (future trajectories) as well as the high-level information (road ...
Rohan Chandra +6 more
semanticscholar +1 more source
Spectral clustering with epidemic diffusion [PDF]
Physical Review E, 2013 6 pages, to appear in Physical Review ...
Smith, Laura M. +4 more
openaire +3 more sources
IEEE Access, 2023 This study embarks on an in-depth analysis of the performance of various kernel functions, namely uniform, epanechnikov, triangular, and gaussian, in window-based and spectral clustering-based models.
Petr Silhavy, Radek Silhavy
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Optimality of Spectral Clustering for Gaussian Mixture Model [PDF]
Annals of Statistics, 2019 Spectral clustering is one of the most popular algorithms to group high dimensional data. It is easy to implement and computationally efficient. Despite its popularity and successful applications, its theoretical properties have not been fully understood.
Matthias Löffler +2 more
semanticscholar +1 more source
, 2023 AbstractIn this paper, we present a spectral clustering approach for clustering three-way data. Three-way data concern data characterized by three modes: n units, p variables, and t different occasions. In other words, three-way data contain a t × p observed matrix for each statistical observation.
Di Nuzzo Cinzia, Ingrassia Salvatore
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Spectral Clustering of Mixed-Type Data
Stats, 2021 Cluster analysis seeks to assign objects with similar characteristics into groups called clusters so that objects within a group are similar to each other and dissimilar to objects in other groups.
Felix Mbuga, Cristina Tortora
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Kernel spectral clustering of large dimensional data [PDF]
, 2016 This article proposes a first analysis of kernel spectral clustering methods in the regime where the dimension $p$ of the data vectors to be clustered and their number $n$ grow large at the same rate.
Benaych-Georges, Florent +1 more
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Partitioning Well-Clustered Graphs: Spectral Clustering Works! [PDF]
SIAM Journal on Computing, 2017 In this paper we study variants of the widely used spectral clustering that partitions a graph into k clusters by (1) embedding the vertices of a graph into a low-dimensional space using the bottom eigenvectors of the Laplacian matrix, and (2) grouping the embedded points into k clusters via k-means algorithms. We show that, for a wide class of graphs,
Zanetti, Luca, Sun, He, Peng, Richard
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Spectral analysis of the Gram matrix of mixture models [PDF]
, 2016 This text is devoted to the asymptotic study of some spectral properties of the Gram matrix $W^{\sf T} W$ built upon a collection $w_1, \ldots, w_n\in \mathbb{R}^p$ of random vectors (the columns of $W$), as both the number $n$ of observations and the ...
Benaych-Georges, Florent +1 more
core +5 more sources