Parallel Algorithms for Combined Regularized Support Vector Machines: Application in Music Genre Classification
This work addresses the computational bottleneck for CR-SVMs in distributed data settings, with applications in music genre classification, but it is incremental as it builds on existing ADMM and regularization methods.
The authors tackled the lack of efficient algorithms for Combined Regularized Support Vector Machines (CR-SVMs) in distributed big data by proposing a unified optimization framework and a distributed parallel ADMM algorithm, which demonstrated reliability, stability, and efficiency in experiments on synthetic and music datasets.
In the era of rapid development of artificial intelligence, its applications span across diverse fields, relying heavily on effective data processing and model optimization. Combined Regularized Support Vector Machines (CR-SVMs) can effectively handle the structural information among data features, but there is a lack of efficient algorithms in distributed-stored big data. To address this issue, we propose a unified optimization framework based on 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 regularization terms, showing strong scalability. Based on this framework, we develop a distributed parallel alternating direction method of multipliers (ADMM) algorithm to efficiently compute CR-SVMs when data is stored in a distributed manner. To ensure the convergence of the algorithm, we also introduce the Gaussian back-substitution method. Meanwhile, for the integrity of the paper, we introduce a new model, the 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 not affected by different regularization terms and loss functions, highlighting the universality of the parallel algorithm. Experiments on synthetic and free music archiv datasets demonstrate the reliability, stability, and efficiency of the algorithm.