Classification with incomplete training information using a cluster ensemble and low-rank matrix decomposition | Zendy

B. A. TULEGENOVA | Zendy; E. N. AMIRGALIYEV | Zendy; L. Cherikbayeva | Zendy; Vladimir Berikov | Zendy

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Classification with incomplete training information using a cluster ensemble and low-rank matrix decomposition

Author(s) -

B. A. TULEGENOVA,

E. N. AMIRGALIYEV,

L. Cherikbayeva,

Vladimir Berikov

Publication year - 2020

Publication title -

bulletin of the national engineering academy of the republic of kazakhstan

Language(s) - English

Resource type - Journals

eISSN - 2709-4707

pISSN - 2709-4693

DOI - 10.47533/2020.1606-146x.32

Subject(s) - computer science , laplacian matrix , regularization (linguistics) , cluster analysis , pattern recognition (psychology) , rank (graph theory) , spectral clustering , matrix decomposition , hyperspectral imaging , computation , artificial intelligence , graph , similarity (geometry) , matrix (chemical analysis) , data mining , algorithm , mathematics , image (mathematics) , theoretical computer science , eigenvalues and eigenvectors , physics , materials science , combinatorics , quantum mechanics , composite material

The paper is devoted to solve the pattern recognition problem with incomplete learning data. The solution method, which combines similarity graph with Laplacian Regularization and collective clustering is proposed. The low-rank decomposition of co-association matrix for cluster ensemble is used, which allows to speed up the computations and keep memory. Experimental results on test tasks and on real hyperspectral image demonstrate the effectiveness of proposed method, including with noisy data.

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