Deep Robust Data Reconstruction via Smoothed L1-Autoencoder and Layerwise Sparse Group Lasso

Junyang Zhang, Wen Yang, Di Ming

Abstract: Compared to conventional linear and low-rank methods, autoencoders demonstrate remarkable capabilities for learning latent feature representations in an unsupervised manner. However, existing approaches, despite improvements through regularization techniques or loss modifications, often remain prone to overfitting on noisy data, thereby limiting their robustness and effectiveness in data reconstruction. Thus, in this paper, we propose a novel deep robust data reconstruction method, dubbed as SL1AE-LSGLasso, to address the challenges of modeling in complex noisy environments. Motivated by the presence of the black spot problem, we first introduce the smoothed L1-autoencoder (SL1AE) network. To enhance model robustness and enable flexible sparsity control, we further propose a layerwise sparse group lasso (LSGLasso) regularization to effectively capture complex nonlinear structures. Building on this, we develop an efficient proximal optimization algorithm to solve the robust data reconstruction problem with composite sparsity-inducing term. Extensive experiments conducted on multiple benchmark datasets validate the effectiveness of the proposed SL1AE-LSGLasso method and its proximal optimization algorithm. In comparison with state-of-the-art methods, our approach improves the performance significantly in data reconstruction and downstream clustering tasks.1

External IDs:dblp:conf/ijcnn/ZhangYM25