Learnable Subspace Orthogonal Transformed Projection for Semi-supervised Image Classification

Lijian Li, Yunhe Zhang, Aiping Huang; Proceedings of the Asian Conference on Computer Vision (ACCV), 2022, pp. 1828-1841

In this paper, we propose a learnable subspace orthogonal projection (LSOP) network for semi-supervised image classification.Although projection theory is widely used in various machine learning methods, solving projection matrix is a highly complex process.We employ an auto-encoder to construct a scalable and learnable subspace orthogonal projection network, thus enjoying lower computational consumption of subspace acquisition and smooth cooperation with deep neural networks. With these techniques, a promising end-to-end classification network is formulated. Extensive experimental results on real-world datasets demonstrate that the proposed classification algorithm achieves comparable performance with fewer training data than other projection methods.