Semi-supervised classification using multiple clustering and low-rank matrix operations

Vladimir Berikov

doi:10.1007/978-3-030-22629-9_37

Semi-supervised classification using multiple clustering and low-rank matrix operations

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › рецензирование

3 Цитирования (Scopus)

Аннотация

This paper proposes a semi-supervised classification method which combines machine learning regularization framework and cluster ensemble approach. We use the low-rank decomposition of the co-association matrix of the ensemble to significantly speed up calculations and save memory. Numerical experiments using Monte Carlo approach demonstrate the efficiency of the proposed method.

Язык оригинала	английский
Название основной публикации	Mathematical Optimization Theory and Operations Research - 18th International Conference, MOTOR 2019, Proceedings
Редакторы	Michael Khachay, Panos Pardalos, Yury Kochetov
Издатель	Springer-Verlag GmbH and Co. KG
Страницы	529-540
Число страниц	12
ISBN (печатное издание)	9783030226282
DOI	https://doi.org/10.1007/978-3-030-22629-9_37
Состояние	Опубликовано - 1 янв 2019
Событие	18th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2019 - Ekaterinburg, Российская Федерация Продолжительность: 8 июл 2019 → 12 июл 2019

Серия публикаций

Название	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Том	11548 LNCS
ISSN (печатное издание)	0302-9743
ISSN (электронное издание)	1611-3349

Конференция

Конференция	18th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2019
Страна	Российская Федерация
Город	Ekaterinburg
Период	08.07.2019 → 12.07.2019

Доступ к документу

10.1007/978-3-030-22629-9_37

Link to publication in Scopus

Fingerprint Подробные сведения о темах исследования «Semi-supervised classification using multiple clustering and low-rank matrix operations». Вместе они формируют уникальный семантический отпечаток (fingerprint).

Цитировать

Berikov, V. (2019). Semi-supervised classification using multiple clustering and low-rank matrix operations. В M. Khachay, P. Pardalos, & Y. Kochetov (Ред.), Mathematical Optimization Theory and Operations Research - 18th International Conference, MOTOR 2019, Proceedings (стр. 529-540). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 11548 LNCS). Springer-Verlag GmbH and Co. KG. https://doi.org/10.1007/978-3-030-22629-9_37

Berikov, Vladimir. / Semi-supervised classification using multiple clustering and low-rank matrix operations. Mathematical Optimization Theory and Operations Research - 18th International Conference, MOTOR 2019, Proceedings. редактор / Michael Khachay ; Panos Pardalos ; Yury Kochetov. Springer-Verlag GmbH and Co. KG, 2019. стр. 529-540 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "This paper proposes a semi-supervised classification method which combines machine learning regularization framework and cluster ensemble approach. We use the low-rank decomposition of the co-association matrix of the ensemble to significantly speed up calculations and save memory. Numerical experiments using Monte Carlo approach demonstrate the efficiency of the proposed method.",

keywords = "Cluster ensemble, Co-association matrix, Low-rank matrix decomposition, Regularization, Semi-supervised classification",

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Berikov, V 2019, Semi-supervised classification using multiple clustering and low-rank matrix operations. в M Khachay, P Pardalos & Y Kochetov (ред.), Mathematical Optimization Theory and Operations Research - 18th International Conference, MOTOR 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), том. 11548 LNCS, Springer-Verlag GmbH and Co. KG, стр. 529-540, 18th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2019, Ekaterinburg, Российская Федерация, 08.07.2019. https://doi.org/10.1007/978-3-030-22629-9_37

Semi-supervised classification using multiple clustering and low-rank matrix operations. / Berikov, Vladimir.

Mathematical Optimization Theory and Operations Research - 18th International Conference, MOTOR 2019, Proceedings. ред. / Michael Khachay; Panos Pardalos; Yury Kochetov. Springer-Verlag GmbH and Co. KG, 2019. стр. 529-540 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 11548 LNCS).

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › рецензирование

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Berikov V. Semi-supervised classification using multiple clustering and low-rank matrix operations. В Khachay M, Pardalos P, Kochetov Y, редакторы, Mathematical Optimization Theory and Operations Research - 18th International Conference, MOTOR 2019, Proceedings. Springer-Verlag GmbH and Co. KG. 2019. стр. 529-540. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-22629-9_37