A Linearly Involved Generalized Moreau Enhancement of ℓ2,1-Norm with Application to Weighted Group Sparse Classification

This paper proposes a new group-sparsity-inducing regularizer to approximate ℓ2,0 pseudo-norm. The regularizer is nonconvex, which can be seen as a linearly involved generalized Moreau enhancement of ℓ2,1-norm. Moreover, the overall convexity of the corresponding group-sparsity-regularized least squares problem can be achieved. The model can handle general group configurations such as weighted group sparse problems, and can be solved through a proximal splitting algorithm. Among the applications, considering that the bias of convex regularizer may lead to incorrect classification results especially for unbalanced training sets, we apply the proposed model to the (weighted) group sparse classification problem. The proposed classifier can use the label, similarity and locality information of samples. It also suppresses the bias of convex regularizer-based classifiers. Experimental results demonstrate that the proposed classifier improves the performance of convex ℓ2,1 regularizer-based methods, especially when the training data set is unbalanced. This paper enhances the potential applicability and effectiveness of using nonconvex regularizers in the frame of convex optimization.

Super RaSE: Super Random Subspace Ensemble Classification

We propose a new ensemble classification algorithm, named Super Random Subspace Ensemble (Super RaSE), to tackle the sparse classification problem. The proposed algorithm is motivated by the Random Subspace Ensemble algorithm (RaSE). The RaSE method was shown to be a flexible framework that can be coupled with any existing base classification. However, the success of RaSE largely depends on the proper choice of the base classifier, which is unfortunately unknown to us. In this work, we show that Super RaSE avoids the need to choose a base classifier by randomly sampling a collection of classifiers together with the subspace. As a result, Super RaSE is more flexible and robust than RaSE. In addition to the vanilla Super RaSE, we also develop the iterative Super RaSE, which adaptively changes the base classifier distribution as well as the subspace distribution. We show the Super RaSE algorithm and its iterative version perform competitively for a wide range of simulated datasets and two real data examples. The new Super RaSE algorithm and its iterative version are implemented in a new version of the R package RaSEn.

Data-driven dictionary design–based sparse classification method for intelligent fault diagnosis of planet bearings

Planet bearings have remained as the challenging components for health monitoring and diagnostics in the planetary transmission systems of helicopters and wind turbines, due to their intricate kinematic mechanisms, strong modulations, and heavy interferences from gear vibrations. To address intelligent diagnostics of planet bearings, this article presents a data-driven dictionary design–based sparse classification (DDD-SC) approach. DDD-SC is free of detecting the weak frequency features and can achieve reliable fault recognition performances for planet bearings without establishing any explicit classifiers. In the first step, DDD-SC implements the data-driven dictionary design with an overlapping segmentation strategy, which leverages the self-similarity features of planet bearing data and constructs the category-specific dictionaries with strong representation power. In the second step, DDD-SC implements the sparsity-based intelligent diagnosis with the sparse representation–based classification criterion and differentiates various planet bearing health states based on minimal sparse reconstruction errors. The effectiveness and superiority of DDD-SC for intelligent planet bearing fault diagnosis have been demonstrated with an experimental planetary transmission system. The extensive diagnosis results show that DDD-SC can achieve the highest diagnosis accuracy, strongest anti-noise performance, and lowest computation costs in comparison with three classical sparse representation–based classification and two advanced deep learning methods.

Deriving accurate molecular indicators of protein synthesis through Raman-based sparse classification

Raman spectroscopy has the ability to retrieve molecular information from live biological samples non-invasively through optical means. Coupled with machine learning, it is possible to use this large amount of...

The Contrast-Enhanced Ultrasound Information Technology in the Treatment of Liver Tumors

Objective: To study the automatic diagnosis method of liver tumors in the contrast-enhanced ultrasound environment, assist doctors in the clinical diagnosis of liver tumors intuitively, conveniently, and accurately, thereby improve the cure rate of liver tumors. Methods: First, six sets of experimental data were constructed. The automatic diagnosis experiment of liver tumors through contrast-enhanced ultrasound was performed by the combination of sparse representation-based support vector machine (SVM) and principal component analysis (PCA)-based SVM, as well as the sparse representation classification method. The effect of classification decision principles on experiments was further studied. Results: The SVM method had an average effect on diagnostic accuracy. The average diagnostic accuracy of the six different experimental data sets was 76%, and the average diagnosis time was 300 s. The feature extraction method based on the combination of sparse representation and PCA was applied to the SVM method to achieve an optimal diagnosis. The average diagnosis accuracy rate could reach 87%, and the average time was more than 1,000 s. Using the sparse classification representation method, the diagnostic accuracy rate for the six experimental data sets constructed was above 93%, with a maximum of 99%, and the average time was 210 s. The sparse classification representation using the principle of minimum reconstruction error classification decision had an average diagnostic accuracy rate of 99% and an average time of 128 s. Conclusion: The sparse classification representation for the clinical diagnosis of liver tumors by contrast-enhanced ultrasound had high accuracy and consumed less time. Therefore, the constructed method was valuable.