Most of estimation of distribution algorithms (EDAs) try to represent explicitly the relationship between variables with factorization techniques or with graphical models such as Bayesian networks. In this paper, we propose to use latent variable models such as Helmholtz machine and probabilistic principal component analysis for capturing the probabilistic distribution of given data. The latent...

Model Selection is a task selecting set of potential models. This method is capable of establishing hidden semantic relations among the observed features, using a number of latent variables. In this paper, the selection of the correct number of latent variables is critical. In the most of the previous researches, the number of latent topics was selected based on the number of invoked classes. T...

This paper explores a fundamental similarity between cognitive models for crying and conceptions of insight, enlightenment or, in the context of art, "aesthetic experience." All of which center on a process of initial discrepancy, followed by schema change, and conclude in a proposed adjustment or "transformation" of one's self image/world-view. Because tears are argued to mark one of the only ...

We consider the problem of learning the parameters of a structured output prediction model, that is, learning to predict elements of a complex interdependent output space that correspond to a given input. Unlike many of the existing approaches, we focus on the weakly supervised setting, where most (or all) of the training samples have only been partially annotated. Given such a weakly supervise...

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...

Disentanglement is a highly desirable property of representation owing to its similarity human understanding and reasoning. Many works achieve disentanglement upon information bottlenecks (IB). Despite their elegant mathematical foundations, the IB branch usually exhibits lower performance. In order provide an insight into problem, we develop annealing test calculate freezing point (IFP), which...

In this paper we discuss existing and new connections between latent variable models from machine learning and tensors (multi-way arrays) from multilinear algebra. A few ideas have been developed independently in the two communities. However, there are still many useful but unexplored links and ideas that could be borrowed from one of the communities and used in the other. We will start our dis...

 Spatial generalized linear mixed models are used commonly for modelling non-Gaussian discrete spatial responses. We present an algorithm for parameter estimation of the models using Laplace approximation of likelihood function. In these models, the spatial correlation structure of data is carried out by random effects or latent variables. In most spatial analysis, it is assumed that rando...