Exploring novel semi-inner product reproducing Kernels in Banach space for robust Kernel methods
Yi Ding, Ying Zhao, Yan Pei
TL;DR
This paper introduces a new type of kernel in Banach spaces to improve the performance of kernel methods.
Contribution
The core contribution is the definition and derivation of semi-inner product reproducing kernels in Banach spaces.
Findings
Semi-inner product reproducing kernels are rigorously defined and supported by theoretical proofs.
Experiments show superior performance of these kernels compared to polynomial reproducing kernels.
The framework addresses limitations of Hilbert space-based kernel methods.
Abstract
Kernel methods are widely applied across various domains; however, structural limitations of reproducing kernels in Hilbert spaces pose significant challenges. Many challenges inherent to Hilbert spaces can be effectively addressed within the framework of Banach spaces. In this work, we define the semi-inner product reproducing kernel Banach space and its reproducing kernels using semi-inner product and bilinear mapping, supported by rigorous proofs. Specific forms of semi-inner product reproducing kernels are derived within the theoretical framework of the semi-inner product reproducing kernel Banach space. This constitutes the core originality of our work and represents its primary contribution. Through illustrative experiments, we validate the effectiveness of semi-inner product reproducing kernels and demonstrate their superior performance compared to polynomial reproducing kernels.
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Taxonomy
TopicsStochastic Gradient Optimization Techniques · Gaussian Processes and Bayesian Inference · 3D Shape Modeling and Analysis