Tracking the Dimensions of Latent Spaces of Gaussian Process Latent Variable Models

被引：0

作者：

Liu, Yuhao ^{[1
]}

Djuric, Petar M. ^{[2
]}

机构：

[1] SUNY Stony Brook, Dept Appl Math & Stat, Stony Brook, NY 11794 USA

[2] SUNY Stony Brook, Dept Elect & Comp Engn, Stony Brook, NY 11794 USA

来源：

2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2022年

关键词：

Latent Variables; Dimension reduction; Gaussian processes; COMPONENTS;

D O I：

10.1109/ICASSP43922.2022.9746538

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Determining the number of latent variables, or the dimensions of latent states, is a ubiquitous problem in dimension reduction. In this paper, we introduce a novel sequential method that relies on the Bayesian approach to estimate the dimension of a latent space of a Gaussian process latent variable model. The proposed method also considers settings where the number of latent variables varies with time. To evaluate our methodology, we compared the estimated dimensions with the true dimensions as they vary with time. Results on synthetic data demonstrate that our method has a very good performance.

引用

页码：4193 / 4197

页数：5

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