Differential Similarity in Higher Dimensional Spaces: Theory and Applications
This work addresses clustering and coding problems in machine learning, but it is incremental as it generalizes an existing method to higher dimensions.
The paper extends differential similarity theory to n-dimensional spaces, developing algorithms for clustering and coding that combine geometric and probabilistic models, and applies them to MNIST and CIFAR-10 datasets.
This paper presents an extension and an elaboration of the theory of differential similarity, which was originally proposed in arXiv:1401.2411 [cs.LG]. The goal is to develop an algorithm for clustering and coding that combines a geometric model with a probabilistic model in a principled way. For simplicity, the geometric model in the earlier paper was restricted to the three-dimensional case. The present paper removes this restriction, and considers the full $n$-dimensional case. Although the mathematical model is the same, the strategies for computing solutions in the $n$-dimensional case are different, and one of the main purposes of this paper is to develop and analyze these strategies. Another main purpose is to devise techniques for estimating the parameters of the model from sample data, again in $n$ dimensions. We evaluate the solution strategies and the estimation techniques by applying them to two familiar real-world examples: the classical MNIST dataset and the CIFAR-10 dataset.