Multi-view subspace clustering, which aims to partition a set of multi-source data into a common space, has recently attracted wide attention in the field of data analysis and machine learning. Traditional algorithms may face the problem of high complexity in calculating the self-expression matrix and in turning parameter. This paper proposes a novel multi-view subspace clustering model termed as Low-rank Multi-view Subspace Clustering via Adaptive Weight (LMSCAW). LMSCAW decomposes the self-expression matrix into the product of two low-rank representation matrices and thus can fix the rank of the self-expression matrix of each view to increase the stability of the algorithm. In addition, in order to learn the common representation matrix better, LMSCAW fuses the self-expression matrices among multiple views and implicitly weights each view by the Frobenius norm without additional parameters. Extensive experimental results on multiple benchmark datasets are provided to show the effectiveness of the proposed algorithm and its superior performance over other state-of-the-art methods.

The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensions, prompting the utilization of machine learning (ML) as a powerful toolkit for extracting knowledge and understanding underlying biological patterns. Kidney disease represents one of the major growing global health threats with intricate pathogenic mechanisms and a lack of precise molecular pathology-based therapeutic modalities. Accordingly, there is a need for advanced high-throughput approaches to capture implicit molecular features and complement current experiments and statistics. This review aims to delineate strategies for integrating multi-omics data with appropriate ML methods, highlighting key clinical translational scenarios, including predicting disease progression risks to improve medical decision-making, comprehensively understanding disease molecular mechanisms, and practical applications of image recognition in renal digital pathology. Examining the benefits and challenges of current integration efforts is expected to shed light on the complexity of kidney disease and advance clinical practice.

For many image processing and computer vision problems, data points are in matrix form. Traditional methods often convert a matrix into a vector and then use vector-based approaches. They will ignore the location of matrix elements and the converted vector often has high dimensionality. How to select features for 2D matrix data directly is still an uninvestigated important issue. In this paper, we propose an algorithm named sparse matrix regression (SMR) for direct feature selection on matrix data. It employs the matrix regression model to accept matrix as input and bridges each matrix to its label. Based on the intrinsic property of regression coefficients, we design some sparse constraints on the coefficients to perform feature selection. An effective optimization method with provable convergence behavior is also proposed. We reveal that the number of regression vectors can be regarded as a tradeoff parameter to balance the capacity of learning and generalization in essence. To examine the effectiveness of SMR, we have compared it with several vector-based approaches on some benchmark data sets. Furthermore, we have also evaluated SMR in the application of scene classification. They all validate the effectiveness of our method.

Feature selection and feature transformation, the two main ways to reduce dimensionality, are often presented separately. In this paper, a feature selection method is proposed by combining the popular transformation-based dimensionality reduction method linear discriminant analysis (LDA) and sparsity regularization. We impose row sparsity on the transformation matrix of LDA through l2,1-norm regularization to achieve feature selection, and the resultant formulation optimizes for selecting the most discriminative features and removing the redundant ones simultaneously. The formulation is extended to the l2,p-norm regularized case, which is more likely to offer better sparsity when 0 < p < 1. Thus, the formulation is a better approximation to the feature selection problem. An efficient algorithm is developed to solve the l2,p-norm-based optimization problem and it is proved that the algorithm converges when 0 < p ≤ 2. Systematical experiments are conducted to understand the work of the proposed method. Promising experimental results on various types of real-world data sets demonstrate the effectiveness of our algorithm.

Regardless of rapid adoption of digitalization in printing production processes, production management in most printing companies is still low efficient and operated in an old fashion. This paper applies the CIM philosophy to the print-manufacturing environment. A Computer Integrated Printing Manufacturing (CIPM) framework is proposed with integration of an Application Architecture, a System Integration Framework and a Job Definition Format (JDF) based Print Process Control Framework. The CIPM framework takes advantage of existing standards such as CIP4/JDF, XML, PDF and other advanced information technologies. These technologies make a CIPM system flexible, scalable and extensible.