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Concept factorization (CF), unlike nonnegative matrix factorization (NMF), can handle data with negative values by approximating the original data with two low-dimensional nonnegative matrices and itself. Nevertheless, existing CF-based methods continue to suffer from the two issues specified as follows: (1) Their effectiveness is reduced by the high degree of factorization freedom and the two-stage mismatch between factorization and category acquisition, and (2) their robustness drops significantly when dealing with complex noise. In response to the aforementioned issues, we propose a robust spectral-embedded bilateral orthogonal concept factorization (RSOCF) model for clustering. It constrains the factor matrices as orthogonal matrices to decrease the freedom and obtain samples' categories directly after factorization, which can significantly improve clustering effectiveness. Moreover, correntropy is introduced into RSOCF to improve its robustness to complex noise. To optimize the non-convex RSOCF model, a half-quadratic-based algorithm is devised. Numerous experiments demonstrate that RSOCF surpasses other state-of-the-art methods in terms of clustering effectiveness and robustness. • A novel robust concept factorization-based clustering model is proposed. • A fast algorithm is proposed to optimize the non-convex model. • Numerous experiments validate its clustering effectiveness and robustness. [ABSTRACT FROM AUTHOR]

Randomized autoencoder (RAE) has attracted much attention due to its strong capability of representation with fast learning speed. However, the mainstream RAEs are still designed for scalar/vector data, which inevitably destroys the structure information of tensor data. To alleviate this deficiency, a novel convolutions based matrix randomized autoencoder (MRAE) is developed for two-dimensional (2D) data in this paper, including a one-side MRAE (OMRAE) exploiting the row or column information and a double-side MRAE (DMRAE) that simultaneously extracts the row and column information by 2 parallel OMRAEs. To reduce meaningless encoded features, the within-class scatter matrix (WSI) and within-class interaction distance (WID) constraints are added into OMRAE resulting WSI-OMRAE and WID-OMRAE, respectively. To demonstrate the superiority, stacked MRAEs are embedded into hierarchical regularized least squares for one-class classification and comparisons with several state-of-the-art methods are provided. The source code would be available at https://github.com/ML-HDU/MRAE. • MRAE can learn from 2D data directly without breaking structure information. • WSI-OMRAE minimizes both reconstruction error and within-class scatter. • WID-OMRAE can learn intrinsic manifold structure with compact representation. • Adopting two-sides OMRAEs can perform row/column feature learning in parallel. [ABSTRACT FROM AUTHOR]

• A novel spatial attention encoder is proposed, which builds global feature insight with long-range dependence to enhance perception. • We propose a context block decoder to aggregate contextual information as channel-specific bias of the features. • A feature fusion module with parallel branches considering expectation and residual property is proposed to realize adaptive information fusion. • We build an ESD dataset with 89.70% small defects collected from real industry an evaluation benchmark. Despite the promising development of Automatic Visual Inspection (AVI) in the manufacturing industry, detecting small-sized defects with fewer pixels coverage remains a challenging problem due to its insufficient attention and lack of semantic information. Most exsiting convolutional inspection methods overlook the long-range dependence of context and lack adaptive fusion strategies to exploit heterogeneous features. To address these issues in AVI, this paper proposes a novel contextual information and spatial attention based network (CANet), which consists of two steps, namely CAblock and LaplacianFPN, for effective perception and exploitation of small defect features. Specifically, CAblock extracts semantic information with rich context by encoding spatial long-range dependence and decoding contextual information as channel-specific bias through a Spatial Attention Encoder (SAE) and a Context Block Decoder (CBD), respectively. LaplacianFPN further performs adaptive feature fusion considering both feature consistency and heterogeneity via two parallel branches. As a benchmark, a self-built Engine Surface Defects (ESD) dataset collected in real industry containing 89.70% small defects is constructed. Experimental results show that CANet achieves mAP-50 improvements of 1.5% and 4.3% compared to state-of-the-art methods on NEU-DET and ESD, which demonstrates the effectiveness of the proposed method. The code is now available at https://github.com/xiuqhou/CANet. [ABSTRACT FROM AUTHOR]

• A novel hypergraph based semi-supervised SNMF method is proposed for image clustering. • A new hypergraph based pairwise constraints propagation algorithm is presented. • The proposed method is analysed in terms of convergence, supervisory information, and computational complexity. • Extensive experiments confirm the effectiveness of the proposed method in clustering applications. Semi-supervised symmetric nonnegative matrix factorization (SNMF) has been shown to be a significant method for both linear and nonlinear data clustering applications. Nevertheless, existing SNMF-based methods only adopt a simple graph to construct the similarity matrix, and cannot fully use the limited supervised information for the construction of the similarity matrix. To overcome the drawbacks of previous SNMF-based methods, a new semi-supervised SNMF-based method called hypergraph based semi-supervised SNMF (HSSNMF), is proposed in this paper for image clustering. Specifically, HSSNMF adopts a predefined hypergraph to build a similarity matrix for capturing the high-order relationships of samples. By exploiting a new hypergraph based pairwise constraints propagation (HPCP) algorithm, HSSNMF propagates the pairwise constraints of the limited data points to the entire data points, which can make full use of the limited supervised information and construct a more informative similarity matrix. Using the multiplicative updating algorithm, a discriminative assignment matrix can then be obtained by solving the optimization problem of HSSNMF. Moreover, analyses of the convergence, supervisory information, and computational complexity of HSSNMF are presented. Finally, extensive clustering experiments have been conducted on six real-world image datasets, and the experimental results have demonstrated the superiority of HSSNMF while compared with several state-of-the-art methods. [ABSTRACT FROM AUTHOR]