We propose a Bayesian method for variable selection in the finite mixture model of linear regressions. The model assumes that the observations come from a heterogeneous population which is a mixture of a finite number of sub-populations. Within each sub-population, the response variable can be explained by a linear regression on the predictor variables. So the whole data set can be modeled by a...

A detection approach, detection with a correlation bound (DCB), is introduced based on a linear mixture model. We use the upper bound of the correlation between the target and mixing components as the detection index, and derive the expression for this correlation bound using the observed data. The proposed method is an unsupervised approach and provides satisfactory detection performance witho...

Simple linear independent component analysis (ICA) algorithms work efficiently only in linear mixing environments. Whereas, a nonlinear ICA model, which is more complicated, would be more practical for general applications as it can work with both linear and nonlinear mixtures. In this paper, we introduce a novel method for nonlinear ICA problem. The proposed method follows the post nonlinear a...

In this paper we present a new density estimation algorithm using mixtures of mixtures of Gaussians. The new algorithm overcomes the limitations of the popular Expectation Maximization algorithm. The paper first introduces a new model selection criterion called the Penaltyless Information Criterion, which is based on the Jensen-Shannon divergence. Mean-shift is used to automatically initialize ...

In this paper we present a generative latent variable model for rating-based collaborative filtering called the User Rating Profile model (URP). The generative process which underlies URP is designed to produce complete user rating profiles, an assignment of one rating to each item for each user. Our model represents each user as a mixture of user attitudes, and the mixing proportions are distr...

For machinery product which experienced several operating conditions, this paper proposes a framework of fuzzy reliability analysis of machinery accelerated testing. Due to the non-stationary of the vibration signals, a Gaussian mixture model (GMM) method is introduced to obtain the degradation index through calculating the overlap between current feature set and the historical baseline set. Th...

In this paper we introduce a method to learn multiple behaviors in the form of motor primitives from an unlabeled dataset. One of the difficulties of this problem is that in the measurement space, behaviors can be very mixed, despite existing a latent representation where they can be easily separated. We propose a mixture model based on a Dirichlet Process (DP) to simultaneously cluster the obs...

In the past 2 decades latent variable modeling has become a standard tool in the social sciences. In the same time period, traditional linear structural equation models have been extended to include non-linear interaction and quadratic effects (e.g., Klein and Moosbrugger, 2000), and multilevel modeling (Rabe-Hesketh et al., 2004). We present a general non-linear multilevel structural equation ...

This study presents a finite element (FE) micromechanical modelling approach for the simulation of linear and damage-coupled viscoelastic behaviour of asphalt mixture. Asphalt mixture is a composite material of graded aggregates bound with mastic (asphalt and fine aggregates). The microstructural model of asphalt mixture incorporates an equivalent lattice network structure whereby intergranular...

把一個來源語者(source speaker)的語音轉換成另一個目標語者(target speaker)的語音,這 種處理稱為語音轉換(voice conversion)[1, 2, 3],語音轉換可應用於銜接語音合成處理, 以獲得多樣性的合成語音音色。去年我們曾嘗試以線性多變量迴歸(linear multivariate regression, LMR)來建構一種頻譜對映(mapping)的機制[4],然後用於作語音轉換,希望 藉以改進傳統上基於高斯混合模型(Gaussian mixture model, GMM)之頻譜對映機制[3] 常遇到的一個問題,就是轉換出的頻譜包絡(spectral envelope)會發生過度平滑(over smoothing)的現象。我們經由實驗發現,音段式(segmental) LMR 頻譜對映機制不僅在平 均轉換誤差上可以比傳統 GMM 頻譜對映機...