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

B. A. TULEGENOVA, E. N. AMIRGALIYEV, Lyailya Cherikbayeva, Vladimir Berikov

2020

Full text Open access: Gold Metadata (CSL-JSON)

DOI 10.47533/2020.1606-146x.32

Data confidence 70 medium

How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

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.