RPN: Reconciled Polynomial Network Towards Unifying PGMs, Kernel SVMs, MLP and KAN

TL;DR

This paper introduces Reconciled Polynomial Network (RPN), a versatile deep model that unifies various machine learning models including PGMs, SVMs, MLPs, and KAN, through a general architecture for improved function approximation.

Contribution

RPN provides a novel unified framework that combines data expansion, parameter reconciliation, and remainder functions to encompass both deep and non-deep models.

Findings

RPN achieves competitive performance across diverse benchmark datasets.

The model effectively unifies different machine learning paradigms.

Empirical results demonstrate RPN's flexibility and accuracy.

Abstract

In this paper, we will introduce a novel deep model named Reconciled Polynomial Network (RPN) for deep function learning. RPN has a very general architecture and can be used to build models with various complexities, capacities, and levels of completeness, which all contribute to the correctness of these models. As indicated in the subtitle, RPN can also serve as the backbone to unify different base models into one canonical representation. This includes non-deep models, like probabilistic graphical models (PGMs) - such as Bayesian network and Markov network - and kernel support vector machines (kernel SVMs), as well as deep models like the classic multi-layer perceptron (MLP) and the recent Kolmogorov-Arnold network (KAN). Technically, RPN proposes to disentangle the underlying function to be inferred into the inner product of a data expansion function and a parameter reconciliation function. Together with the remainder function, RPN accurately approximates the underlying functions that governs data distributions. The data expansion functions in RPN project data vectors from the input space to a high-dimensional intermediate space, specified by the expansion functions in definition. Meanwhile, RPN also introduces the parameter reconciliation functions to fabricate a small number of parameters into a higher-order parameter matrix to address the ``curse of dimensionality'' problem caused by the data expansions. Moreover, the remainder functions provide RPN with additional complementary information to reduce potential approximation errors. We conducted extensive empirical experiments on numerous benchmark datasets across multiple modalities, including continuous function datasets, discrete vision and language datasets, and classic tabular datasets, to investigate the effectiveness of RPN.