This paper focuses on service clustering and uses service descriptions to construct probabilistic models for service clustering. We discuss how service descriptions can be enriched with machine-interpretable semantics and then we investigate how these service descriptions can be grouped in clusters in order to make discovery, ranking, and recommendation faster and more effective. We propose usi...

Probabilistic latent topic models have recently enjoyed much success in extracting and analyzing latent topics in text in an unsupervised way. One common deficiency of existing topic models, though, is that they would not work well for extracting cross-lingual latent topics simply because words in different languages generally do not co-occur with each other. In this paper, we propose a way to ...

Not only in social sciences but also in chemometrics, the paths (e.g., regression coefficients, correlations) between latent variables are often estimated by regarding the estimated latent variable scores as observed variables. Such methods are often called “factor score regression”. Recently partial least square (PLS) path modeling is used for the same purpose. Similarly, the latent variable s...

In its most basic form, latent growth modeling (latent curve analysis) allows an assessment of individuals’change in a measured variable X over time. For simple linear models, as with other growth models, parameter estimates associated with the α construct (amount of X at a chosen temporal reference point) and β construct (growth in X per unit time) are not invariant with respect to choice of r...

OBJECTIVE Pediatric psychologists are often interested in finding patterns in heterogeneous longitudinal data. Latent variable mixture modeling is an emerging statistical approach that models such heterogeneity by classifying individuals into unobserved groupings (latent classes) with similar (more homogenous) patterns. The purpose of the second of a 2-article set is to offer a nontechnical int...

A spatial latent class analysis model that extends the classic latent class analysis model by adding spatial structure to the latent class distribution through the use of the multinomial probit model is introduced. Linear combinations of independent Gaussian spatial processes are used to develop multivariate spatial processes that are underlying the categorical latent classes. This allows the l...

In this paper we present a fully Bayesian latent variable model which exploits conditional nonlinear (in)-dependence structures to learn an efficient latent representation. The latent space is factorized to represent shared and private information from multiple views of the data. In contrast to previous approaches, we introduce a relaxation to the discrete segmentation and allow for a “softly” ...

We introduce a novel kernel that models input-dependent couplings across multiple latent processes. The pairwise joint kernel measures covariance along inputs and across different latent signals in a mutually-dependent fashion. A latent correlation Gaussian process (LCGP) model combines these non-stationary latent components into multiple outputs by an input-dependent mixing matrix. Probit clas...

Herpes simplex virus type 1 (HSV-1) establishes a latent infection in neurons of the peripheral nervous system. During latent HSV-1 infection, viral gene expression is limited to latency-associated transcripts (LAT). HSV-1 remains latent until an unknown mechanism induces reactivation. The ability of the latent virus to periodically reactivate and be shed is essential to the transmission of dis...

Despite many years of potent antiretroviral therapy, latently infected cells and low levels of plasma virus have been found to persist in HIV-infected patients. The factors influencing this persistence and their relative contributions have not been fully elucidated and remain controversial. Here, we address these issues by developing and employing a simple, but mechanistic viral dynamics model....