42828

Автор(ы): 

Автор(ов): 

2

Параметры публикации

Тип публикации: 

Статья в журнале/сборнике

Название: 

Robust Principal Component Analysis: An IRLS Approach

Наименование источника: 

  • IFAC-PapersOnLine

Обозначение и номер тома: 

Volume 50, Issue 1

Город: 

  • Toulouse

Издательство: 

  • IFAC

Год издания: 

2017

Страницы: 

2762-2767
Аннотация
The modern problems of optimization, estimation, signal processing, and image recognition deal with data of huge dimensions. It is important to develop effective methods and algorithms for such problems. An important idea is the construction of low-dimension approximations to large-scale data. One of the most popular methods for this purpose is the principal component analysis (PCA), which is, however, sensitive to outliers. There exist numerous robust versions of PCA, relying on sparsity ideas and ℓ1 techniques. The present paper offers another approach to robust PCA exploiting Huber’s functions and numerical implementation based on the Iterative Reweighted Least Squares (IRLS) method.

Библиографическая ссылка: 

Поляк Б.Т., Хлебников М.В. Robust Principal Component Analysis: An IRLS Approach // IFAC-PapersOnLine. 2017. Volume 50, Issue 1. С. 2762-2767.