Results 51 to 60 of about 232,287 (178)
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
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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
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Hearing the clusters in a graph: A distributed algorithm [PDF]
, 2011 We propose a novel distributed algorithm to cluster graphs. The algorithm recovers the solution obtained from spectral clustering without the need for expensive eigenvalue/vector computations.
Banaszuk, Andrzej +2 more
core
Spectral clustering for jet physics
Journal of High Energy Physics, 2022 We present a new approach to jet definition alternative to clustering methods, such as the anti-kT scheme, that exploit kinematic data directly. Instead the new method uses kinematic information to represent the particles in a multidimensional space, as ...
Giorgio Cerro +5 more
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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
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Sensors, 2022 Hyperspectral imaging can simultaneously acquire spectral and spatial information of the samples and is, therefore, widely applied in the non-destructive detection of grain quality.
Zhen Kang +5 more
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Making Laplacians commute [PDF]
, 2013 In this paper, we construct multimodal spectral geometry by finding a pair of closest commuting operators (CCO) to a given pair of Laplacians. The CCOs are jointly diagonalizable and hence have the same eigenbasis.
Bronstein, Michael M. +2 more
core
Tensor Spectral Clustering for Partitioning Higher-order Network Structures
, 2015 Spectral graph theory-based methods represent an important class of tools for studying the structure of networks. Spectral methods are based on a first-order Markov chain derived from a random walk on the graph and thus they cannot take advantage of ...
Benson, Austin R. +2 more
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Large Scale Spectral Clustering Using Approximate Commute Time Embedding
, 2012 Spectral clustering is a novel clustering method which can detect complex shapes of data clusters. However, it requires the eigen decomposition of the graph Laplacian matrix, which is proportion to $O(n^3)$ and thus is not suitable for large scale ...
C. Fowlkes +10 more
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An Improvement of Spectral Clustering via Message Passing and Density Sensitive Similarity
IEEE Access, 2019 Spectral clustering transforms the data clustering problem into a graph-partitioning problem and classifies data points by finding the optimal sub-graphs. Traditional spectral clustering algorithms use Gaussian kernel function to construct the similarity
Lijuan Wang, Shifei Ding, Hongjie Jia
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