Weakly Supervised Regression Using Manifold Regularization and Low-Rank Matrix Representation

Vladimir Berikov, Alexander Litvinenko

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

Аннотация

We solve a weakly supervised regression problem. Under “weakly” we understand that for some training points the labels are known, for some unknown, and for others uncertain due to the presence of random noise or other reasons such as lack of resources. The solution process requires to optimize a certain objective function (the loss function), which combines manifold regularization and low-rank matrix decomposition techniques. These low-rank approximations allow us to speed up all matrix calculations and reduce storage requirements. This is especially crucial for large datasets. Ensemble clustering is used for obtaining the co-association matrix, which we consider as the similarity matrix. The utilization of these techniques allows us to increase the quality and stability of the solution. In the numerical section, we applied the suggested method to artificial and real datasets using Monte-Carlo modeling.

Язык оригиналаанглийский
Название основной публикацииMathematical Optimization Theory and Operations Research - 20th International Conference, MOTOR 2021, Proceedings
РедакторыPanos Pardalos, Michael Khachay, Alexander Kazakov
ИздательSpringer Science and Business Media Deutschland GmbH
Страницы447-461
Число страниц15
ISBN (печатное издание)9783030778750
DOI
СостояниеОпубликовано - 2021
Событие20th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2021 - Irkutsk, Российская Федерация
Продолжительность: 5 июл 202110 июл 2021

Серия публикаций

НазваниеLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Том12755 LNCS
ISSN (печатное издание)0302-9743
ISSN (электронное издание)1611-3349

Конференция

Конференция20th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2021
СтранаРоссийская Федерация
ГородIrkutsk
Период05.07.202110.07.2021

Предметные области OECD FOS+WOS

  • 1.02 КОМПЬЮТЕРНЫЕ И ИНФОРМАЦИОННЫЕ НАУКИ
  • 1.01 МАТЕМАТИКА

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