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Pseudo-orthogonality for graph 1-Laplacian eigenvectors and applications to higher Cheeger constants and data clustering
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作者 Antonio Corbo ESPOSITO Gianpaolo PISCITELLI 《Frontiers of Mathematics in China》 SCIE CSCD 2022年第4期591-623,共33页
The data clustering problem consists in dividing a data set into prescribed groups of homogeneous data.This is an NP-hard problem that can be relaxed in the spectral graph theory,where the optimal cuts of a graph are ... The data clustering problem consists in dividing a data set into prescribed groups of homogeneous data.This is an NP-hard problem that can be relaxed in the spectral graph theory,where the optimal cuts of a graph are related to the eigenvalues of graph 1-Laplacian.In this paper,we first give new notations to describe the paths,among critical eigenvectors of the graph 1-Laplacian,realizing sets with prescribed genus.We introduce the pseudo-orthogonality to characterize m_(3)(G),a special eigenvalue for the graph 1-Laplacian.Furthermore,we use it to give an upper bound for the third graph Cheeger constant h_(3)(G),that is,h_(3)(G)≤m_(3)(G).This is a first step for proving that the k-th Cheeger constant is the minimum of the 1-Laplacian Raylegh quotient among vectors that are pseudo-orthogonal to the vectors realizing the previous k−1 Cheeger constants.Eventually,we apply these results to give a method and a numerical algorithm to compute m3(G),based on a generalized inverse power method. 展开更多
关键词 Graph 1-Laplacian graph Cheeger constants pseudo-orthogonality critical values data clustering
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