Structure preserving projections learning via low-rank embedding for image classification
Structure preserving projections learning via low-rank embedding for image classification
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DOI：
10.1016/j.ins.2023.119636
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发表时间：
2023-09
期刊：
影响因子：
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通讯作者：
Mingxiu Cai;M. Wan;Guowei Yang;Zhangjing Yang;Hao Zheng;Hai Tan;Mingwei Tang
中科院分区：
文献类型：
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作者：
Mingxiu Cai;M. Wan;Guowei Yang;Zhangjing Yang;Hao Zheng;Hai Tan;Mingwei Tang
子空间映射是特征提取和选择的关键步骤和重要工具。尽管子空间映射方法是成功的，但仍存在一些共同的缺陷：1）最终子空间不具有全局性质，仅具有局部性质; 2）大多数模型未能保留低秩结构信息; 3）投影矩阵包含大量冗余和无关信息。因此，我们开发了一种新的方法，即，通过低秩嵌入的结构保持投影学习（SPPL-LRE）来解决上述问题。首先通过寻找最大方差的投影方向来提取主成分信息，使最终的子空间具有全局性质。然后，SPPL-LRE将主成分信息回归到类块对角结构。通过这种方式，数据的低秩信息和主要能量被保持在子空间中，使得所获得的投影可以提取更显著和信息量更大的特征。同时，引入低秩信息也可以增强模型的鲁棒性。此外，对投影施加强L2范数约束，以避免模型过拟合，排除冗余信息的干扰，从而增强模型的可解释性。最后，我们引入了表示矩阵的图光滑性，以更好地保持原始数据的流形几何。大量的实验表明，我们的方法是更强大的和有效的比其他国家的最先进的方法。
Subspace mapping is a key step and an important tool for feature extraction and selection. Although subspace mapping methods are successful, there are still some common defects: 1) The final subspace does not possess global properties, only local properties; 2) most models fail to retain the low-rank structural information; and 3) the projection matrix contains considerable redundant and irrelevant information. Accordingly, we develop a novel method, i.e., structure preserving projections learning via low-rank embedding (SPPL-LRE), to address the above issues. We first extract the principal component information by seeking the projection directions of maximum variance, which makes the final subspace equipped with global properties. Then, SPPL-LRE regresses the principal component information to a classwise block-diagonal structure. In this way, the low-rank information and main energy of the data are held in the subspace so that the obtained projection can extract more salient and informative features. Meanwhile, introducing low-rank information can also enhance the robustness of the model. Moreover, a strong L2 norm constraint is imposed on the projection to avoid model overfitting and exclude the interference of redundant information, which can enhance the interpretability of the model. Finally, we introduce the graph smoothness of the representation matrix to better preserve the manifold geometry of the original data. Extensive experiments indicate that our method is more robust and effective than other state-of-the-art methods.