Eigenvalue based spectral classification
This paper describes a new method of classification based on spectral analysis. The motivations behind developing the new model were the failures of the classical spectral cluster analysis based on combinatorial and normalized Laplacian for a set of real-world datasets of textual documents. Reasons...
Main Authors: | Piotr Borkowski, Mieczysław A. Kłopotek, Bartłomiej Starosta, Sławomir T. Wierzchoń, Marcin Sydow |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2023-01-01
|
Series: | PLoS ONE |
Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10079090/?tool=EBI |
Similar Items
-
Eigenvalue based spectral classification.
by: Piotr Borkowski, et al.
Published: (2023-01-01) -
WEIGHTED LAPLACIANS OF GRIDS AND THEIR APPLICATION FOR INSPECTION OF SPECTRAL GRAPH CLUSTERING METHODS
by: MIECZYSŁAW KŁOPOTEK, et al.
Published: (2021-07-01) -
Estimates of complex eigenvalues and an inverse spectral problem for the transmission eigenvalue problem
by: Xiao-Chuan Xu, et al.
Published: (2019-06-01) -
Wide Gaps and Kleinberg’s Clustering Axioms for k–Means
by: Kłopotek Mieczysław A.
Published: (2024-03-01) -
The spectral function and principal eigenvalues for Schrodinger operators
by: Arendt, W, et al.
Published: (1997)