The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction. It has an extremely low training complexity while achieving state-of-the-art classification performance. In this work, we improve the PointHop method furthermore in two aspects: 1) reducing its model complexity in terms of the model parameter number and 2) ordering discriminant features automatically based on the cross-entropy criterion. The resulting method is called PointHop++. The first improvement is essential for wearable and mobile computing while the second improvement bridges statistics-based and optimization-based machine learning methodologies. With experiments conducted on the ModelNet40 benchmark dataset, we show that the PointHop++ method performs on par with deep neural network (DNN) solutions and surpasses other unsupervised feature extraction methods.
@article{arxiv.2002.03281,
title = {PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification},
author = {Min Zhang and Yifan Wang and Pranav Kadam and Shan Liu and C. -C. Jay Kuo},
journal= {arXiv preprint arXiv:2002.03281},
year = {2020}
}