The 54th Statistical Machine Learning Seminar

【Date & Time】
Jan. 12, 2023 (Thu) 16:00 - 17:00

Admission Free

ISM Seminar room 5 (3rd floor) + Zoom (Hybrid seminar)
Please register at the following Google Forms. A Zoom link will be emailed to you.
Michael Minyi Zhang
Latent variable modeling with random features
Gaussian process-based latent variable models are flexible and theoretically grounded tools for nonlinear dimension reduction, but generalizing to non-Gaussian data likelihoods within this nonlinear framework is statistically challenging. Here, we use random features to develop a family of nonlinear dimension reduction models that are easily extensible to non-Gaussian data likelihoods; we call these random feature latent variable models (RFLVMs). By approximating a nonlinear relationship between the latent space and the observations with a function that is linear with respect to random features, we induce closed-form gradients of the posterior distribution with respect to the latent variable. This allows the RFLVM framework to support computationally tractable nonlinear latent variable models for a variety of data likelihoods in the exponential family without specialized derivations. Our generalized RFLVMs produce results comparable with other state-of-the-art dimension reduction methods on diverse types of data, including neural spike train recordings, images, and text data.