Building Bridges between Regression, Clustering, and Classification

TL;DR

This paper introduces a novel approach to regression tasks in machine learning by leveraging ideas from classification and clustering, aiming to improve neural network training and performance.

Contribution

The authors propose a new method using a target encoder and prediction decoder to enhance regression model training, bridging regression with classification and clustering techniques.

Findings

Improved regression performance on real-world datasets.

The method outperforms traditional mean squared error minimization.

Demonstrates versatility across diverse datasets.

Abstract

Regression, the task of predicting a continuous scalar target y based on some features x is one of the most fundamental tasks in machine learning and statistics. It has been observed and theoretically analyzed that the classical approach, meansquared error minimization, can lead to suboptimal results when training neural networks. In this work, we propose a new method to improve the training of these models on regression tasks, with continuous scalar targets. Our method is based on casting this task in a different fashion, using a target encoder, and a prediction decoder, inspired by approaches in classification and clustering. We showcase the performance of our method on a wide range of real-world datasets.