@article{9f2322f6b96948839b7448292f0c470c,
title = "Diagonal principal component analysis with non-greedy ℓ1-norm maximization for face recognition",
abstract = "Diagonal principal component analysis (DiaPCA) is an important method for dimensionality reduction and feature extraction. It usually makes use of the ℓ2-norm criterion for optimization, and is thus sensitive to outliers. In this paper, we present a DiaPCA with non-greedy ℓ1-norm maximization (DiaPCA-L1 non-greedy), which is more robust to outliers. Experimental results on two benchmark datasets show the effectiveness and advantages of our proposed method.",
keywords = "Diagonal PCA, Face recognition, Non-greedy strategy, Principal component analysis (PCA), ℓ-norm",
author = "Qiang Yu and Rong Wang and Xiaojun Yang and Li, {Bing Nan} and Minli Yao",
note = "Publisher Copyright: {\textcopyright} 2015 Elsevier B.V.",
year = "2016",
month = jan,
day = "1",
doi = "10.1016/j.neucom.2015.06.011",
language = "英语",
volume = "171",
pages = "57--62",
journal = "Neurocomputing",
issn = "0925-2312",
publisher = "Elsevier B.V.",
}