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Integrated Low-Rank-Based Discriminative Feature Learning for Recognition

Zhou, Pan ; Lin, Zhouchen ; Zhang, Chao

IEEE transaction on neural networks and learning systems, 2016-05, Vol.27 (5), p.1080-1093

United States: IEEE

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  • Título:
    Integrated Low-Rank-Based Discriminative Feature Learning for Recognition
  • Autor: Zhou, Pan ; Lin, Zhouchen ; Zhang, Chao
  • Assuntos: Algorithm design and analysis ; Classification ; Closed-form solutions ; Feature learning ; Learning systems ; low-rank representation (LRR) ; Matrix decomposition ; Principal component analysis ; recognition ; robust principal component analysis (PCA) ; Robustness
  • É parte de: IEEE transaction on neural networks and learning systems, 2016-05, Vol.27 (5), p.1080-1093
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: Feature learning plays a central role in pattern recognition. In recent years, many representation-based feature learning methods have been proposed and have achieved great success in many applications. However, these methods perform feature learning and subsequent classification in two separate steps, which may not be optimal for recognition tasks. In this paper, we present a supervised low-rank-based approach for learning discriminative features. By integrating latent low-rank representation (LatLRR) with a ridge regression-based classifier, our approach combines feature learning with classification, so that the regulated classification error is minimized. In this way, the extracted features are more discriminative for the recognition tasks. Our approach benefits from a recent discovery on the closed-form solutions to noiseless LatLRR. When there is noise, a robust Principal Component Analysis (PCA)-based denoising step can be added as preprocessing. When the scale of a problem is large, we utilize a fast randomized algorithm to speed up the computation of robust PCA. Extensive experimental results demonstrate the effectiveness and robustness of our method.
  • Editor: United States: IEEE
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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