# A Variant of Gaussian Process Dynamical Systems

## TL;DR

This paper introduces a collaborative multi-output Gaussian process dynamical system (CGPDS) that models high-dimensional sequential data by capturing dependencies among dimensions through shared and private latent processes, using inducing points and variational inference.

## Contribution

It presents a novel variant of GPDS that effectively models multi-dimensional sequences with shared and individual characteristics, improving upon existing methods.

## Key findings

- Successfully captures dependencies among sequence dimensions.
- Efficient training with inducing points and variational inference.
- Enhances modeling of high-dimensional sequential data.

## Abstract

In order to better model high-dimensional sequential data, we propose a collaborative multi-output Gaussian process dynamical system (CGPDS), which is a novel variant of GPDSs. The proposed model assumes that the output on each dimension is controlled by a shared global latent process and a private local latent process. Thus, the dependence among different dimensions of the sequences can be captured, and the unique characteristics of each dimension of the sequences can be maintained. For training models and making prediction, we introduce inducing points and adopt stochastic variational inference methods.