Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel

• URL: http://arxiv.org/abs/2309.08201v2
• Date: Tue, 26 Dec 2023 17:35:32 GMT
• Title: Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel
• Authors: Richard Cornelius Suwandi, Zhidi Lin, Feng Yin, Zhiguo Wang, Sergios Theodoridis
• Abstract summary: This paper presents a novel GP linear multiple kernel (LMK) and a generic sparsity-aware distributed learning framework. The framework incorporates a quantized alternating direction method of multipliers (ADMM) for collaborative learning among multiple agents. Experiments on diverse datasets demonstrate the superior prediction performance and efficiency of our proposed methods.
• Score: 22.23550794664218
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gaussian processes (GPs) stand as crucial tools in machine learning and signal processing, with their effectiveness hinging on kernel design and hyper-parameter optimization. This paper presents a novel GP linear multiple kernel (LMK) and a generic sparsity-aware distributed learning framework to optimize the hyper-parameters. The newly proposed grid spectral mixture (GSM) kernel is tailored for multi-dimensional data, effectively reducing the number of hyper-parameters while maintaining good approximation capabilities. We further demonstrate that the associated hyper-parameter optimization of this kernel yields sparse solutions. To exploit the inherent sparsity property of the solutions, we introduce the Sparse LInear Multiple Kernel Learning (SLIM-KL) framework. The framework incorporates a quantized alternating direction method of multipliers (ADMM) scheme for collaborative learning among multiple agents, where the local optimization problem is solved using a distributed successive convex approximation (DSCA) algorithm. SLIM-KL effectively manages large-scale hyper-parameter optimization for the proposed kernel, simultaneously ensuring data privacy and minimizing communication costs. Theoretical analysis establishes convergence guarantees for the learning framework, while experiments on diverse datasets demonstrate the superior prediction performance and efficiency of our proposed methods.

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