In this paper, we propose an unsupervised learning algorithm for Natural Language Processing (NLP). In particular, we present a sentiment analysis solution using probabilistic models. We propose two new smoothed probabilistic-based models that incorporate the benefits of smoothing techniques and scaling word vectors to address sparseness and high-dimensionality challenges. We introduce the smoothed scaled Dirichlet and the smoothed shifted scaled Dirichlet mixture models, the learning approach for the mixture parameters, the clustering algorithms, and the sentiment analysis framework. We consider in our experiments different benchmarks of sentiment analysis, namely, Stanford Twitter sentiment (STD), Stanford sentiment gold standard (STS-Gold), SemEval2014 Task9, and Sentiment Strength Twitter (SentiStrength). The results are compared with the baselines and state-of-the-art (SOTA) models in the literature where our proposed approaches outperform all the other models.

Projection and Clustering are two main approaches in text mining. The goal of projection is to map the high-dimensional data into a lower-dimensional latent space, where the clustering task is to categorize data into different groups based on their similarity features. Several methods have been proposed to retrieve relevant information based on the co-occurrence of the data. However, the majority of works do not examine the joint effect of the projection and clustering, especially the effect of the prior distribution in the case of discrete data. For this purpose, in this paper, we propose a novel approach using a probabilistic clustering-projection framework where Dirichlet distribution, generalized Dirichlet distribution, and Beta-Liouville distribution are implemented as priors to study the impacts of prior knowledge in the perplexity of the model. Using a variational EM algorithm we estimate latent variables associated with clustering and projection parameters, iteratively updating the lower bound of the log-likelihood until convergence. Our experimental results demonstrate reduced perplexity using generalized Dirichlet and Beta-Liouville priors compared to Dirichlet. Moreover, we evaluate the model's performance in word projection and document clustering tasks, finding that both generalized Dirichlet and Beta-Liouville outperform Dirichlet in these domains.

Abstract Short text clustering is one of the fundamental tasks in natural language processing. Different from traditional documents, short texts are ambiguous and sparse due to their short form and the lack of recurrence in word usage from one text to another, making it very challenging to apply conventional machine learning algorithms directly. In this article, we propose two novel approaches for short texts clustering: collapsed Gibbs sampling infinite generalized Dirichlet multinomial mixture model infinite GSGDMM) and collapsed Gibbs sampling infinite Beta‐Liouville multinomial mixture model (infinite GSBLMM). We adopt two flexible and practical priors to the multinomial distribution where in the first one the generalized Dirichlet distribution is integrated, while the second one is based on the Beta‐Liouville distribution. We evaluate the proposed approaches on two famous benchmark datasets, namely, Google News and Tweet. The experimental results demonstrate the effectiveness of our models compared to basic approaches that use Dirichlet priors. We further propose to improve the performance of our methods with an online clustering procedure. We also evaluate the performance of our methods for the outlier detection task, in which we achieve accurate results.

In this paper, we propose a nonparametric Bayesian approach based on a mixture of the exponential-approximation to the Dirichlet Compound Multinomial (EDCM), which has been shown to be very flexible and efficient for high-dimensional sparse count data modeling. Our approach can be viewed as the first attempt to extend the finite EDCM mixture model to the infinite case, which allows the elicitation of prior belief about the parameters and the number of clusters through Markov Chain Monte Carlo sampling within Metropolis-Hastings. This extension provides a natural representation of uncertainty regarding the challenging problem of model selection, where the problems of overfitting and underfitting are prevented due to the nature of the nonparametric Bayesian approach. The resulting statistical model is applied to a challenging application, namely human interaction recognition.

Along with the exponential growth of online video creation platforms such as Tik Tok and Instagram, state of the art research involving quick and effective action/gesture recognition applications remains crucial. This work addresses the challenge of classifying such short video clips, using a domain-specific feature design approach, capable of performing significantly well using little training data. The method is based on Gunner Farneback dense optical flow (GF-OF) estimation strategy, Gaussian mixture models, and information divergence. We first aim to obtain accurate 3D representations of the human movements/actions through clustering the results given by GF-OF using K-means method of vector quantization. We then proceed by representing the result of one instance of each action by a Gaussian mixture model. Furthermore, using Kullback-Leibler divergence (KL-divergence), we attempt to find similarities between the trained actions and the ones in the test videos. Classification is done by matching each testing video to the trained action with the highest similarity (lowest KL-divergence). We have performed experiments on the KTH and Weizmann Human Action datasets, and the results reveal the discriminative nature of our proposed methodology in comparison with other state of the art techniques.

In this paper, we address the problem of data clustering into homogeneous components in an unsupervised way. Data clustering is one of the major topics in computer vision which has widespread potential applications from various domains such as pattern recognition, data mining, remote sensing, and bioinformatics. In pattern recognition, statistical methods have been widely used and proved effective in generating accurate models. In particular, the popular finite Gaussian mixture models which are able to provide superior performance for data clustering and classification. In this work, we present and evaluate the performance of four well-known Gaussian-based mixture models for data clustering namely: Gaussian mixture model (GMM), Generalized Gaussian mixture model (GGMM), Bounded Gaussian mixture model (BGMM) and Bounded Generalized Gaussian mixture model (BGGMM). The aim of this work is to show that the choice of the component model is very critical in mixture decomposition. Experimental results show close clustering accuracy between different models. However, the bounded generalized Gaussian mixture model provides the best performance in the case of multidimensional data.