Joint Low-Rank and Sparse Principal Feature Coding for Enhanced Robust Representation and Visual Classification
Joint Low-Rank and Sparse Principal Feature Coding for Enhanced Robust Representation and Visual Classification
复制标题
用于增强鲁棒表示和视觉分类的联合低秩和稀疏主特征编码
DOI：
10.1109/tip.2016.2547180
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发表时间：
2016-06
影响因子：
10.6
通讯作者：
Yan Shuicheng
中科院分区：
文献类型：
--
作者：
Zhang Zhao;Li Fanzhang;Zhao Mingbo;Zhang Li;Yan Shuicheng
讨论了联合恢复低秩子空间和稀疏子空间以增强稳健表示和分类的方法。在技术上，我们首先提出了一种换能式低阶稀疏主特征编码(LSPFC)公式，它将给定的数据分解为编码低阶稀疏主特征的分量和噪声拟合误差部分。为了更好地处理外部数据，我们提出了一种感应式LSPFC(I-LSPFC)。I-LSPFC通过投影将嵌入的低阶和稀疏的主特征合并到一个问题中进行直接最小化，从而使投影能够有效地将内外数据映射到底层的子空间中，以学习更强大和更丰富的特征来表示。为了确保I-LSPFC学习到的特征是最优的，我们进一步将分类误差和特征编码误差结合起来，形成一个统一的模型--判别LSPFC(D-LSPFC)，以提高性能。D-LSPFC模型将特征编码和判别分类有机地结合在一起，提高了表示能力和分类能力。所提出的方法更具一般性，最近已有的几种低阶或稀疏编码算法可以作为特例嵌入到我们的问题中。可视化和数值结果证明了我们的表示和分类方法的有效性。
Recovering low-rank and sparse subspaces jointly for enhanced robust representation and classification is discussed. Technically, we first propose a transductive low-rank and sparse principal feature coding (LSPFC) formulation that decomposes given data into a component part that encodes low-rank sparse principal features and a noise-fitting error part. To well handle the outside data, we then present an inductive LSPFC (I-LSPFC). I-LSPFC incorporates embedded low-rank and sparse principal features by a projection into one problem for direct minimization, so that the projection can effectively map both inside and outside data into the underlying subspaces to learn more powerful and informative features for representation. To ensure that the learned features by I-LSPFC are optimal for classification, we further combine the classification error with the feature coding error to form a unified model, discriminative LSPFC (D-LSPFC), to boost performance. The model of D-LSPFC seamlessly integrates feature coding and discriminative classification, so the representation and classification powers can be enhanced. The proposed approaches are more general, and several recent existing low-rank or sparse coding algorithms can be embedded into our problems as special cases. Visual and numerical results demonstrate the effectiveness of our methods for representation and classification.
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DOI：
10.1109/tpami.2005.55
发表时间：
2005-03-01
影响因子：
23.6
作者：
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