Jingwen Tu | Searcharxiv

arXiv2026

Parallel Algorithms for Structured Sparse Support Vector Machines: Application in Music Genre Classification

Mathematical modelling, particularly through approaches such as structured sparse support vector machines (SS-SVM), plays a crucial role in processing data with complex feature structures, yet efficient algorithms for distributed large-scale data remain lacking. To address this gap, this paper proposes a unified optimization framework based on a consensus structure. This framework is not only applicable to various loss functions and combined regularization terms but can also be effectively extended to non-convex regularizers, demonstrating strong scalability. Building upon this framework, we develop a distributed parallel alternating direction method of multipliers (ADMM) algorithm to efficiently solve SS-SVMs under distributed data storage. To ensure convergence, we incorporate a Gaussian back-substitution technique. Additionally, for completeness, we introduce a family of sparse group Lasso support vector machine (SGL-SVM) and apply it to music information retrieval. Theoretical analysis confirms that the computational complexity of the proposed algorithm is independent of the choice of regularization terms and loss functions, underscoring the universality of the parallel approach. Experiments on both synthetic and real-world music archive datasets validate the reliability, stability, and efficiency of our algorithm.

cs.LG↗

arXiv2018

Semiparametric model averaging for high dimensional conditional quantile prediction

In this article, we propose a penalized high dimensional semiparametric model average quantile prediction approach that is robust for forecasting the conditional quantile of the response. We consider a two-step estimation procedure. In the first step, we use a local linear regression approach to estimate the individual marginal quantile functions, and approximate the conditional quantile of the response by an affine combination of one-dimensional marginal quantile regression functions. In the second step, based on the nonparametric kernel estimates of the marginal quantile regression functions, we utilize a penalized method to estimate the suitable model weights vector involved in the approximation. The objective of the second step is to select significant variables whose marginal quantile functions make a significant contribution to estimating the joint multivariate conditional quantile function. Under some mild conditions, we have established the asymptotic properties of the proposed robust estimator. Finally, simulations and a real data analysis have been used to illustrate the proposed method.

math.ST↗