Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference

2022-02-25 · Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence

Gaussian process latent variable models (GPLVM) are a flexible and non-linear approach to dimensionality reduction, extending classical Gaussian processes to an unsupervised learning context. The Bayesian incarnation of the GPLVM Titsias and Lawrence, 2010] uses a variational framework, where the posterior over latent variables is approximated by a well-behaved variational family, a factorized Gaussian yielding a tractable lower bound. However, the non-factories ability of the lower bound prevents truly scalable inference. In this work, we study the doubly stochastic formulation of the Bayesian GPLVM model amenable with minibatch training. We show how this framework is compatible with different latent variable formulations and perform experiments to compare a suite of models. Further, we demonstrate how we can train in the presence of massively missing data and obtain high-fidelity reconstructions. We demonstrate the model's performance by benchmarking against the canonical sparse GPLVM for high-dimensional data examples.

📄 PDF Abstract BibTeX arXiv:2202.12979

Code (0)

등록된 구현이 없습니다.

Tasks

Benchmarking Dimensionality Reduction Gaussian ProcessesVariational Inference

Bayesian Non-linear Latent Variable Modeling via Random Fourier Features

2023-06-14 · Michael Minyi Zhang, Gregory W. Gundersen, Barbara E. Engelhardt

The Gaussian process latent variable model (GPLVM) is a popular probabilistic method used for nonlinear dimension reduction, matrix factorization, and state-space modeling. Inference for GPLVMs is computationally tractab…

Dimensionality Reduction

Gaussian Process Latent Variable Flows for Massively Missing Data

2020-11-23 · pproximateinference AABI Symposium 2021 1 · Vidhi Lalchand, Aditya Ravuri, Neil D Lawrence

Gaussian process latent variable models (GPLVM) are used to perform nonlinear and probabilistic dimensionality reduction. They extend Gaussian processes (GP) to the domain of unsupervised learning. The Bayesian incarnati…

Dimensionality Reduction Gaussian Processes Normalising Flows Variational Inference

Multimodal Gaussian Process Latent Variable Models With Harmonization

2017-10-01 · ICCV 2017 10 · Guoli Song, Shuhui Wang, Qingming Huang, Qi Tian

In this work, we address multimodal learning problem with Gaussian process latent variable models (GPLVMs) and their application to cross-modal retrieval. Existing GPLVM based studies generally impose individual priors o…

Cross-Modal Retrieval Retrieval

Preventing Model Collapse in Gaussian Process Latent Variable Models

2024-04-02 · Ying Li, Zhidi Lin, Feng Yin, Michael Minyi Zhang

Gaussian process latent variable models (GPLVMs) are a versatile family of unsupervised learning models commonly used for dimensionality reduction. However, common challenges in modeling data with GPLVMs include inadequa…

Dimensionality Reduction Imputation Variational Inference

Harmonized Multimodal Learning with Gaussian Process Latent Variable Models

2019-08-14 · Guoli Song, Shuhui Wang, Qingming Huang, Qi Tian

Multimodal learning aims to discover the relationship between multiple modalities. It has become an important research topic due to extensive multimodal applications such as cross-modal retrieval. This paper attempts to …