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Deep image clustering networks have the capability to categorize unlabeled images, thereby effectively utilizing them. This paper synthesizes recent researches about deep image clustering network and summarizes them within a general framework. Image Preprocessing part transforms collected images into a dataset accepted by the network, then Image Embedding part embeds images from the dataset into vectors which represents image features. After Feature Processing part further reduces the dimensionality or enhances these features, the following Feature Clustering part divides the images into several categories. Cluster Result Processing part treats the clustering outcomes to implement weighted, multi-view, subspace, multimodal, or self-supervised methods. Downstream Applications part employ the clustering results to address various real-world problems. The performance of common baseline clustering methods and several feature extraction architectures are also compared and analyzed. The results indicate that within deep image clustering networks, the choice of clustering algorithm has a relatively minor impact on clustering performance, whereas the selection of the feature extraction network architecture is decisive for clustering metrics. The choice of architecture should be customized based on the characteristics of the dataset. Finally, we provide suggestions for potential research directions in deep image clustering networks. • Analyzes deep image clustering theory, its performance benefits, and limitations. • Summarizes recent deep image clustering improvements with experimental data analysis. • Introduces deep image clustering applications and future improvement prospects. [ABSTRACT FROM AUTHOR]

Twin support vector machines (TWSVM) is a new machine learning method based on the theory of Support Vector Machine (SVM). Unlike SVM, TWSVM would generate two non-parallel planes, such that each plane is closer to one of the two classes and is as far as possible from the other. In TWSVM, a pair of smaller sized quadratic programming problems (QPPs) is solved, instead of solving a single large one in SVM, making the computational speed of TWSVM approximately 4 times faster than the standard SVM. At present, TWSVM has become one of the popular methods because of its excellent learning performance. In this paper, the research progress of TWSVM is reviewed. Firstly, it analyzes the basic theory of TWSVM, then tracking describes the research progress of TWSVM including the learning model and specific applications in recent years, finally points out the research and development prospects. This helps researchers to effectively use TWSVM as an emerging research approach, encouraging them to work further on performance improvement. [ABSTRACT FROM AUTHOR]

Recently, some nonparallel plane regressors, such as twin support vector regression (TSVR), and least squares TSVR (LSTSVR), have been proposed and have attracted much attention. However, these algorithms are not sparse, which would make their learning speed low. In this paper, we propose a novel nonparallel plane regressor, which can automatically select the relevant features. Firstly, we introduce a regularization term to the objective function of LSTSVR, which can guarantee two quadratic programming problems (QPPs) are strong convex, implying that the proposed algorithm can obtain the global but unique solution. Secondly, the primal formulation is converted to a linear programming (LP) problem. Then, we solve the dual of the LP formulation by minimizing its exterior penalty problem, which would make our method yield very sparse solutions. In other words, this method can suppress input features so that it can obtain comparable regression performance when using fewer computational time. Numerical experiments on artificial dataset and benchmark datasets demonstrate the feasibility and validity of the proposed algorithm. [ABSTRACT FROM AUTHOR]