Estimating and interpreting latent variable interactions

Estimating Latent-Variable Graphical Models using Moments and Likelihoods

Recent work on the method of moments enable consistent parameter estimation, but only for certain types of latent-variable models. On the other hand, pure likelihood objectives, though more universally applicable, are difficult to optimize. In this work, we show that using the method of moments in conjunction with composite likelihood yields consistent parameter estimates for a much broader cla...

Explicit estimating equations for semiparametric generalized linear latent variable models

We study generalized linear latent variable models without requiring a distributional assumption of the latent variables. Using a geometric approach, we derive consistent semiparametric estimators. We demonstrate that these models have a property which is similar to that of a sufficient complete statistic, which enables us to simplify the estimating procedure and explicitly to formulate the sem...

Estimating latent feature-feature interactions in large feature-rich graphs

Complex networks arising in nature are usually modeled as (directed or undirected) graphs describing some connection between the objects that are identified with their nodes. In many real-world scenarios, though, those objects are endowed with properties and attributes (hereby called features). In this paper, we shall confine our interest to binary features, so that every node has a precise set...

Evaluating And Interpreting Interactions

Shaking Hands in Latent Space - Modeling Emotional Interactions with Gaussian Process Latent Variable Models

We present an approach for the generative modeling of human interactions with emotional style variations. We employ a hierarchical Gaussian process latent variable model (GP-LVM) to map motion capture data of handshakes into a space of low dimensionality. The dynamics of the handshakes in this low dimensional space are then learned by a standard hidden Markov model, which also encodes the emoti...