Multimodal latent variable analysis

Multimodal Latent Variable Analysis

Consider a set of multiple, multimodal sensors capturing a complex system or a physical phenomenon of interest. Our primary goal is to distinguish the underlying sources of variability manifested in the measured data. The first step in our analysis is to find the common source of variability present in all sensor measurements. We base our work on a recent paper, which tackles this problem with ...

Latent Class Analysis Variable Selection.

We propose a method for selecting variables in latent class analysis, which is the most common model-based clustering method for discrete data. The method assesses a variable's usefulness for clustering by comparing two models, given the clustering variables already selected. In one model the variable contributes information about cluster allocation beyond that contained in the already selected...

Latent variable measurement models and path analysis

What is a latent variable? • A variable that is not observable or is not directly measurable. • A variable that is measured with error or can only be measured with error. • A latent variable can be used to represent a 'true' variable which is measured with error, OR a single conceptual variable, OR a construct which is a summarization of a complex concept. Examples of 'true' variables that are ...

Latent Variable Models for Hippocampal Sequence Analysis

VIRTUAL TUNING CURVES we only train the HMMs on spikes from PBEs; to determine if the inferred states encode position data, we compute virtual tuning curves in two ways: (A) by decoding RUN data using the PBE-only HMM, and then using the true position data to estimate a map from states to position, and (B) by using the Bayesian decoder to estimate position during PBEs, and to learn a map from t...

Latent Variable Models and Factor Analysis