Semi-supervised classification with cluster ensemble

Vladimir Berikov, Nikita Karaev, Ankit Tewari

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

5 Цитирования (Scopus)

Аннотация

We propose a method for semi-supervised classification using a combination of ensemble clustering and kernel based learning. The method works in two steps. In the first step, a number of variants of clustering partition are obtained with some clustering algorithm working on both labeled and unlabeled data. Weighted averaged co-association matrix is calculated using the results of partitioning. We prove that this matrix satisfies Mercer's condition, i.e., it defines symmetric non-negative definite kernel. In the second step, a decision function is constructed on labeled data using the obtained matrix as kernel. Some theoretical properties of the proposed method related to its convergence to the optimal classifier are investigated. Numerical experiments show that the proposed method possesses accuracy comparable with some state of the art methods, and in many cases outperforms them. We will illustrate the performance of the method on the problems of semi-supervised classification of hyperspectral images.

Язык оригиналаанглийский
Название основной публикацииProceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017
ИздательInstitute of Electrical and Electronics Engineers Inc.
Страницы245-250
Число страниц6
ISBN (электронное издание)9781538615966
DOI
СостояниеОпубликовано - 14 ноя 2017
Событие2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017 - Novosibirsk, Российская Федерация
Продолжительность: 18 сен 201722 сен 2017

Конференция

Конференция2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017
СтранаРоссийская Федерация
ГородNovosibirsk
Период18.09.201722.09.2017

Fingerprint Подробные сведения о темах исследования «Semi-supervised classification with cluster ensemble». Вместе они формируют уникальный семантический отпечаток (fingerprint).

  • Цитировать

    Berikov, V., Karaev, N., & Tewari, A. (2017). Semi-supervised classification with cluster ensemble. В Proceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017 (стр. 245-250). [8109880] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIBIRCON.2017.8109880