Spectral clustering algorithm using density-sensitive distance measure with global and local consistencies-Reference-Cited by-同舟云学术

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Spectral clustering algorithm using density-sensitive distance measure with global and local consistencies

Published:2019-04 Issue: Volume:170 Page:26-42
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Tao Xinmin,Wang Ruotong,Chang Rui,Li Chenxi,Liu Rui,Zou Junrong

Funder

Fundamental Research Funds for the Central Universities

Innovative talent fund of Harbin science and technology Bureau

Double first-class scientific research foundation of Northeast Forestry University

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference52 articles.

1. Graph regularized non-negative low-rank matrix factorization for image clustering;Li;IEEE Trans. Cybern.,2017

2. Machine learning of linear differential equations using Gaussian processes;Raissi;J. Comput. Phys.,2017

3. Pattern recognition and classification of two cancer cell lines by diffraction imaging at multiple pixel distances;Wang;Pattern Recognit.,2017

4. Cloud implementation of the K-Means algorithm for hyperspectral image analysis;Haut;J. Supercomput.,2017

5. Efficient label collection for image datasets via hierarchical clustering;Wigness;Int. J. Comput. Vis.,2017

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