An Overview of Robust Subspace Recovery
作者： Gilad Lerman , Tyler Maunu
DOI: 10.1109/JPROC.2018.2853141
关键词: Machine learning 、 Computer science 、 Outlier 、 Artificial intelligence 、 State (computer science) 、 Subspace topology 、 Work (electrical)
摘要: This paper will serve as an introduction to the body of work on robust subspace recovery. Robust recovery involves finding underlying low-dimensional in a dataset that is possibly corrupted with outliers. While this problem easy state, it has been difficult develop optimal algorithms due its nonconvexity. emphasizes advantages and disadvantages proposed approaches unsolved problems area.
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