Hierarchical Density Factorization with Chocloton

Density Estimation using Multi-Agent Optimization & Rewards

Introduction

The purpose, problem statement, and potential applications came from this post on datasciencecentral.com. The goal is to approximate any multi-variate distribution using a weighted sum of kernels. Here, a kernel refers to a parameterized distribution. This method of using a decaying weighted sum of kernels to approximate a distribution is similar to a Taylor series where a function can be approximated, around a point, using the function’s derivatives.

Goals

• Approximate any empirical distribution
• Build a parameterized density estimator
• Outlier detection and dataset noise reduction

The Approach

This solution I came up with was incorporated into a python package, Chocloton. The example code can be found here.

My solution uses the following:
1. Particle Swarm\Genetic Optimizer
2. Multi-Agent Approximation using IID Kernels
3. Reinforcement Learning

Use Cases

• With Density Factorization you can get multivariate density estimate, cluster assignment, and similar data points all in one shot.
• After training, the HDRE model can assign a matrix of…