Due to the semantic gap problem across different modalities, automatically retrieval from
multimedia information still faces a main challenge. It is desirable to provide an effective joint …

A new depth-based clustering procedure for directional data is proposed. Such method is
fully non-parametric and has the advantages to be flexible and applicable even in high …

This paper investigates to identify the requirement and the development of machine learning-
based mobile big data analysis through discussing the insights of challenges in the mobile …

We address the problem of blind source separation in acoustic applications where there is
no prior knowledge about the number of mixing sources. The presented method employs a …

From the most known Gaussian mixture to the cutting-edge multi-Bernoulli mixture of various
forms, mixture offers a fundamental means to deal with uncertainties, which has led to a …

Modern probabilistic learning systems mainly assume symmetric distributions, however, real-
world data typically obey skewed distributions and are thus not adequately modeled through …

In this paper, we focus on a variational Bayesian learning approach to infinite Dirichlet
mixture model (VarInDMM) which inherits the confirmed effectiveness of modeling …

The relative timing of sleep and of eating within the circadian day is important for human
health. Despite much data on sleep, and a growing data set for eating, there remains a need …

The Generative Adversarial Networks (GANs) are deep generative models that can generate
realistic samples, but they are difficult to train in practice due to the problem of mode …

Due to advances in technology, there is a presence of directional data in a wide variety of
fields. Often distributions to model directional data are defined on manifold or constrained …