Efficient Estimation for Shared Latent Space Using Multi-layer Perceptron

摘要

There are quite a few high dimensional time-series data co-ocurring each other such as lip motions, voices, and face appearances and so on. When capturing the correspondent relationships among those time-series data with different dimensionality, we need to make the dimensionality all the same size so that they can be compared each other. To achieve this, Gaussian Process Latent Variable Models (GPLVM) is often used to reduce the size of high dimensional time-series data. In this study, we propose a method to introduce MLP to GPLVM-based methods in estimating latent states. We applied the proposed method to GPLVM, SharedGPLVM, GPDM, and SharedGPDM, and then confirmed that our method outperforms the conventional methods in terms of efficiency and precisely estimation.