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Metric learning has attracted a lot of interest in classification tasks due to its efficient performance. Most traditional metric learning methods are based on k-nearest neighbors (kNN) classifiers to make decisions, while the choice k affects the generalization. In this work, we propose an end-to-end metric learning framework. Specifically, a new linear metric learning (LMML) is first proposed to jointly learn adaptive metrics and the optimal classification hyperplanes, where dissimilar samples are separated by maximizing classification margin. Then a nonlinear metric learning model (called RLMML) is developed based on a bound nonlinear kernel function to extend LMML. The non-convexity of the proposed models makes them difficult to optimize. The half-quadratic optimization algorithms are developed to solve iteratively the problems, by which the optimal classification hyperplane and adaptive metric are alternatively optimized. Moreover, the resulting algorithms are proved to be convergent theoretically. Numerical experiments on different types of data sets show the effectiveness of the proposed algorithms. Finally, the Wilcoxon test shows also the feasibility and effectiveness of the proposed models., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

We propose a new homotopy loss, where practitioners can tune the parameter to derive different loss functions such as l 1 -norm loss, logarithmic loss, Geman-Reynolds loss, Geman-McClure loss and correntropy-based loss. Moreover, we illustrate that the proposed loss satisfies Fisher consistency, and we analyze the robustness of the proposed homotopy loss from different perspectives: M-estimation and adversarial perturbations. Then, we represent a new evaluation standard to measure robustness and demonstrate its upper bound to ensure the validity of this measure. Applied the proposed homotopy loss to least square support vector machine (LSSVM) and the extreme learning machine (ELM), two robust models are presented to enhance the robustness. Furthermore, re-weighted least square algorithm is used to solve the problems, and the resulting algorithms converge globally. In addition, the proposed methods are implemented on various datasets with different levels of noise. Compared with traditional methods, experiment results on real-world datasets show that the proposed methods have superior anti-interference ability to outliers in most cases., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Circular RNA (circRNA) is an important member of non-coding RNA family. Numerous computational methods for detecting circRNAs from RNA-seq data have been developed in the past few years, but there are dramatic differences among the algorithms regarding the balancing of the sensitivity and precision of the detection and filtering strategies. To further improve the sensitivity, while maintaining an acceptable precision of circRNA detection, a novel and efficient de novo detection algorithm, CIRCPlus, is proposed in this paper. CIRCPlus accurately locates circRNA candidates by identifying a set of back-spliced junction reads by comparing the local similar sequence of each pair of spanning junction reads. This strategy, thus, utilizes the important information provided by unbalanced spanning reads, which facilitates the detection especially when the expression levels of circRNA are unapparent. The performance of CIRCPlus was tested and compared to the existing de novo methods on the real datasets as well as a series of simulation datasets with different configurations. The experiment results demonstrated that the sensitivities of CIRCPlus were able to reach 90% in common simulation settings, while CIRCPlus held balanced sensitivity and reliability on the real datasets according to an objective assessment criteria based on RNase R-treated samples. The software tool is available for academic uses only.

Background. Non-small-cell lung cancer (NSCLC) is a major health problem that endangers human health. The prognosis of radiotherapy or chemotherapy is still unsatisfactory. This study is aimed at investigating the predictive value of glycolysis-related genes (GRGs) on the prognosis of NSCLC patients with radiotherapy or chemotherapy. Methods. Download the clinical information and RNA data of NSCLC patients receiving radiotherapy or chemotherapy from TCGA and geo databases and obtain GRGs from MsigDB. The two clusters were identified by consistent cluster analysis, the potential mechanism was explored by KEGG and GO enrichment analyses, and the immune status was evaluated by estimate, TIMER, and quanTIseq algorithms. Lasso algorithm is used to build the corresponding prognostic risk model. Results. Two clusters with different GRG expression were identified. The high-expression subgroup had poor overall survival. The results of KEGG and GO enrichment analyses suggest that the differential genes of the two clusters are mainly reflected in metabolic and immune-related pathways. The risk model constructed with GRGs can effectively predict the prognosis. The nomogram combined with the model and clinical characteristics has good clinical application potential. Conclusion. In this study, we found that GRGs are associated with tumor immune status and can assess the prognosis of NSCLC patients receiving radiotherapy or chemotherapy. [ABSTRACT FROM AUTHOR]

Ensemble learning is an effective technique to improve performance and stability compared to single classifiers. This work proposes a selective ensemble classification strategy to handle missing data classification, where an uncertain extreme learning machine with probability constraints is used as individual (or base) classifiers. Then, three selective ensemble frameworks are developed to optimize ensemble margin distributions and aggregate individual classifiers. The first two are robust ensemble frameworks with the proposed loss functions. The third is a sparse ensemble classification framework with the zero-norm regularization, to automatically select the required individual classifiers. Moreover, the majority voting method is applied to produce ensemble classifier for missing data classification. We demonstrate some important properties of the proposed loss functions such as robustness, convexity and Fisher consistency. To verify the validity of the proposed methods for missing data, numerical experiments are implemented on benchmark datasets with missing feature values. In experiments, missing features are first imputed by using expectation maximization algorithm. Numerical experiments are simulated in filled datasets. With different probability lower bounds of classification accuracy, experimental results under different proportion of missing values show that the proposed ensemble methods have better or comparable generalization compared to the traditional methods in handling missing-value data classifications. [ABSTRACT FROM AUTHOR]

• Perform simultaneously subspace learning and metric learning (psub). • Enhance the robustness based on Lp-norm (0 < p < = 2). • Analyze the robustness and the advantage of the psub theoretically. • Design a modified gradient ascending algorithm for psub. • Psub achieves better performance than other algorithms in most cases. Distance metric learning has been an important technique in machine learning field recently due to its high effectiveness in improving the performance of distance related methods. In order to take advantages of both subspace learning and metric learning to overcome the limitations of metric learning, in this work we intend to learn a robust discriminative subspace and a distance metric simultaneously by maximizing the ratio of inter-class covariance to inner-class covariance using l p − n o r m (0 < p ≤ 2) , where the l p − n o r m is used to enhance the robustness. The proposed model is a more general framework compared to the state-of-art algorithms. Moreover, a modified gradient ascending algorithm is designed to optimize the problem, and the convergence of the algorithm and complexity are analyzed. To verify the proposed method, we carry out numerical experiments on artificial data sets and benchmark data sets. Under different evaluation criterions, experiment results show that the proposed method achieves better performance than the state-of-art algorithms in most cases. [ABSTRACT FROM AUTHOR]