Variational learning for Dirichlet process mixtures of Dirichlet distributions and applications

In this paper, we propose a Bayesian nonparametric approach for modeling and selection based on a mixture of Dirichlet processes with Dirichlet distributions, which can also be seen as an infinite Dirichlet mixture model. The proposed model uses a stick-breaking representation and is learned by a variational inference method. Due to the nature of Bayesian nonparametric approach, the problems of overfitting and underfitting are prevented. Moreover, the obstacle of estimating the correct number of clusters is sidestepped by assuming an infinite number of clusters. Compared to other approximation techniques, such as Markov chain Monte Carlo (MCMC), which require high computational cost and whose convergence is difficult to diagnose, the whole inference process in the proposed variational learning framework is analytically tractable with closed-form solutions. Additionally, the proposed infinite Dirichlet mixture model with variational learning requires only a modest amount of computational power which makes it suitable to large applications. The effectiveness of our model is experimentally investigated through both synthetic data sets and challenging real-life multimedia applications namely image spam filtering and human action videos categorization.     

Rheological,thermal, and morphological properties of blends based on poly(propylene), ethylene propylene rubber,and ethylene-1-octene copolymer that could result from end of life vehicles: Effect of maleic anhydride grafted poly(propylene)

The objective of this work is to study the properties of blends that could result from the recycling of end-of-life vehicles (ELV). While ethylene propylene rubber (EPR) and ethylene propylene diene monomer (EPDM) have been used extensively as elastomeric additives in poly(propylene) (PP), they can be substituted by ethylene-1-octene copolymer (EOC). As a consequence, the matter resulting from the sorting of ELV might be more complex and made of PP, EPR, and EOC. The effect of incorporating EOC [that is a polyethylene elastomer (PEE)] and maleic anhydride grafted polypropylene (PP-g-MAH) on the rheological, thermal, and morphological properties of PP/EPR blends has been investigated. Blends of various compositions (with and without compatibilizer) were prepared using a corotating twin-screw extruder. The results were compared to the ones presented by a commercial (PP/EPR) blend. The EPR phase is dispersed in the form of spherical particles in (PP/EPR). The EOC phase is dispersed in the form of aggregated particles. Dynamic viscoelastic and differential scanning calorimetry properties of (PP/EPR)/EOC blends shows the incompatibility of the components even in presence of PP-g-MAH copolymer. POLYM. ENG. SCI., 47:1009–1015, 2007. © 2007 Society of Plastics Engineers     

Supervised collaboration for syntactic annotation of Quranic Arabic

The Quranic Arabic Corpus (http://corpus.quran.com) is a collaboratively constructed linguistic resource initiated at the University of Leeds, with multiple layers of annotation including part-of-speech tagging, morphological segmentation (Dukes and Habash 2010) and syntactic analysis using dependency grammar (Dukes and Buckwalter 2010). The motivation behind this work is to produce a resource that enables further analysis of the Quran, the 1,400 year-old central religious text of Islam. This project contrasts with other Arabic treebanks by providing a deep linguistic model based on the historical traditional grammar known as i′rāb (?????). By adapting this well-known canon of Quranic grammar into a familiar tagset, it is possible to encourage online annotation by Arabic linguists and Quranic experts. This article presents a new approach to linguistic annotation of an Arabic corpus: online supervised collaboration using a multi-stage approach. The different stages include automatic rule-based tagging, initial manual verification, and online supervised collaborative proofreading. A popular website attracting thousands of visitors per day, the Quranic Arabic Corpus has approximately 100 unpaid volunteer annotators each suggesting corrections to existing linguistic tagging. To ensure a high-quality resource, a small number of expert annotators are promoted to a supervisory role, allowing them to review or veto suggestions made by other collaborators. The Quran also benefits from a large body of existing historical grammatical analysis, which may be leveraged during this review. In this paper we evaluate and report on the effectiveness of the chosen annotation methodology. We also discuss the unique challenges of annotating Quranic Arabic online and describe the custom linguistic software used to aid collaborative annotation.     

Discrete data clustering using finite mixture models

Finite mixture models have been applied for different computer vision, image processing and pattern recognition tasks. The majority of the work done concerning finite mixture models has focused on mixtures for continuous data. However, many applications involve and generate discrete data for which discrete mixtures are better suited. In this paper, we investigate the problem of discrete data modeling using finite mixture models. We propose a novel, well motivated mixture that we call the multinomial generalized Dirichlet mixture. The novel model is compared with other discrete mixtures. We designed experiments involving spatial color image databases modeling and summarization, and text classification to show the robustness, flexibility and merits of our approach.     

An inverse Black–Scholes problem

Optimization and Engineering - In this work, we provide a new Black–Scholes model, where the weak formulation at stake is done in the case of a general class of finite Radon measures. A...     

Flexible Distribution-Based Regression Models for Count Data: Application to Medical Diagnosis

Abstract

Data mining techniques have been successfully utilized in different applications of significant fields, including medical research. With the wealth of data available within the health-care systems, there is a lack of practical analysis tools to discover hidden relationships and trends in data. The complexity of medical data that is unfavorable for most models is a considerable challenge in prediction. The ability of a model to perform accurately and efficiently in disease diagnosis is extremely significant. Thus, the model must be selected to fit the data better, such that the learning from previous data is most efficient, and the diagnosis of the disease is highly accurate. This work is motivated by the limited number of regression analysis tools for multivariate counts in the literature. We propose two regression models for count data based on flexible distributions, namely, the multinomial Beta-Liouville and multinomial scaled Dirichlet, and evaluated the proposed models in the problem of disease diagnosis. The performance is evaluated based on the accuracy of the prediction which depends on the nature and complexity of the dataset. Our results show the efficiency of the two proposed regression models where the prediction performance of both models is competitive to other previously used regression models for count data and to the best results in the literature.     

A nonparametric Bayesian learning model using accelerated variational inference and feature selection

Pattern Analysis and Applications - Developing effective machine learning methods for multimedia data modeling continues to challenge computer vision scientists. The capability of providing...     

Novel approaches for synthesizing video textures

Video texture, a novel type of medium, can produce a new video with a continuously varying stream of images from a recorded video. A classic approach to generate video textures is to apply principal components analysis (PCA) for dimensionality reduction (i.e. extraction of frame signatures) and autoregressive (AR) process for prediction purposes. In this paper we investigate the use of other dimensionality reduction techniques to generate accurate video textures. Based on our experiments, the quality of video textures can be improved further. We also propose a new approach for generating video textures using probabilistic principal components analysis (PPCA) and Gaussian process dynamical model (GPDM) to synthesize video textures which contain frames that never appeared before and with similar motions as original videos. Furthermore, we propose two ways of generating online video textures by applying the incremental Isomap and incremental Spatio-temporal Isomap (IST-Isomap). Both approaches can produce good online video texture results. In particular, IST-Isomap, that we propose, is more suitable for sparse video data (e.g. cartoon).