The key problems in visual object classification are: learning discriminative feature to distinguish between two or more visually similar categories ( e.g. dogs and cats), modeling the variation of visual appearance within instances of the same class (e.g. Dalmatian and Chihuahua in the same category of dogs), and tolerate imaging distortion (3D pose). These account to within and between class ...

The EM algorithm has been used repeatedly to identify latent classes in categorical data by estimating finite distribution mixtures of product components. Unfortunately, the underlying mixtures are not uniquely identifiable and, moreover, the estimated mixture parameters are starting-point dependent. For this reason we use the latent class model only to define a set of “elementary” classes by e...

The latent class clustering and segmentation-based models are employed to account for heterogeneity across different groups. Further, the random parameter variants of these modeling frameworks consider within group. Both approaches have recently gained significant attention in road safety literature. However, similarities differences between two methods seldom explained investigated. To that en...

In 2009, Yu et al. proposed a multimod al probability model (MPM) for clustering. This paper makes advanced clustering constructions on the MPM. We first reconstruct most existing clustering algorithms, such as the k-means, fuzzy c-means, possibilistic c-means, mean shift, classification maximum likelihood, and latent class methods, by establishing the relationships between these clustering alg...

In this paper, we propose a novel unsupervised clustering approach exploiting the hidden information that is indirectly introduced through a pseudo classification objective. Specifically, we randomly assign a pseudo parent-class label to each observation which is then modified by applying the domain specific transformation associated with the assigned label. Generated pseudo observation-label p...

ÐWe present a general framework for data analysis and visualization by means of topographic organization and clustering. Imposing distributional assumptions on the assumed underlying latent factors makes the proposed model suitable for both visualization and clustering. The system noise will be modeled in parametric form, as a member of the exponential family of distributions and this allows us...

In this paper, a probabilistic speaker-class (PSC) based acoustic modeling method is proposed for taking into account speaker variability influence in HMM-based speech recognition systems. Firstly, within the context of speaker-class based speech recognition, an experiment is conducted to investigate the performance of speaker-class recognition based on hard-cut speaker clustering. Then, in the...