A robust elastic net approach for feature learning

作者：Wang, Ling; Cheng, Hong^*; Liu, Zicheng; Zhu, Ce

来源：Journal of Visual Communication and Image Representation, 2014, 25(2): 313-321.

DOI：10.1016/j.jvcir.2013.11.002

• Feature learning
• Principal component analysis
• Elastic net
• Spars representation
• Robust statistics
• Object recognition
• Background reconstruction
• Maximum likelihood estimation

Unsupervised feature learning has drawn more and more attention especially in visual representation in past years. Traditional feature learning approaches assume that there are few noises in training data set, and the number of samples is enough compared with the dimensions of samples. Unfortunately, these assumptions are violated in most of visual representation scenarios. In these cases, many feature learning approaches are failed to extract the important features. Toward this end, we propose a Robust Elastic Net (REN) approach to handle these problems. Our contributions are twofold. First of all, a novel feature learning approach is proposed to extract features by weighting elastic net. A distribution induced weight function is used to leverage the importance of different samples thus reducing the effects of outliers. Moreover, the REN feature learning approach can handle High Dimension, Low Sample Size (HDLSS) issues. Second, a REN classifier is proposed for object recognition, and can be used for generic visual representation including that from the REN feature extraction. By doing so, we can reduce the effect of outliers in samples. We validate the proposed REN feature learning and classifier on face recognition and background reconstruction. The experimental results showed the robustness of this proposed approach for both corrupted/occluded samples and HDLSS issues.

• 出版日期2014-2
• 单位电子科技大学