Structure preserving projections learning via low-rank embedding for image classification

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, SPPLLRE 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.

Automated summary

The proposed method aims to address the common defects of subspace mapping methods by introducing a novel structure preserving projections learning via low-rank embedding (SPPL-LRE) algorithm. It achieves this by extracting principal component information, regressing it to classwise block-diagonal structure, and imposing a strong L2 norm constraint on the projection. The method is shown to be more robust and effective than other state-of-the-art methods through extensive experiments.