Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

State-of-the-art in artificial neural network applications: A survey

Traditional and recent approaches in background modeling for foreground detection: An overview

In real-world recognition/classification tasks, limited by various objective factors, it is usually difficult to collect training samples to exhaust all classes when training a recognizer or classifier. A more realistic scenario is open set recognition (OSR), where incomplete knowledge of the world exists at training time, and unknown classes can be submitted to an algorithm during testing, requiring the classifiers to not only accurately classify the seen classes, but also effectively deal with unseen ones. This paper provides a comprehensive survey of existing open set recognition techniques covering various aspects ranging from related definitions, representations of models, datasets, evaluation criteria, and algorithm comparisons. Furthermore, we briefly analyze the relationships between OSR and its related tasks including zero-shot, one-shot (few-shot) recognition/learning techniques, classification with reject option, and so forth. Additionally, we also review the open world recognition which can be seen as a natural extension of OSR. Importantly, we highlight the limitations of existing approaches and point out some promising subsequent research directions in this field.

Recent Advances in Open Set Recognition: A Survey

In this paper we present a sophisticated variational Bayes framework for learning infinite Beta-Liouville mixture models. A key feature of the proposed framework is that the appropriate mixture model complexity can be discovered automatically from the data to cluster as part of the inference procedure. Another important advantage is that the whole inference process itself is analytically tractable with closed-form solutions. Moreover, the problems of over-fitting and under-fitting are also prevented thanks to the nonparametric Bayesian nature of the proposed framework. The effectiveness of our statistical framework is investigated on two challenging motion recognition tasks including hand gesture and human activity recognition.

Spatio-temporal object recognition using variational learning of an infinite statistical model

In this paper, we propose a predictive model based on Beta-Liouville (BL) and inverted Beta-Liouville (IBL) mixture models for occupancy estimation in smart buildings. This model gives better results than point estimate methods, when the training data is small, because it is based on data-driven predictive distribution. The Liouville-based mixture models were chosen because of their flexibility in fitting symmetric and asymmetric distributions. However, the large number of parameters of BL and IBL increases the uncertainty in approximating the upper bound of the predictive distribution, hence we propose an optimization scheme in which reliability is investigated and verified. In addition, we extend our work presented in 1 by giving more details about the predictive model and by studying the occupancy estimation in smart buildings problems in depth. Indeed, different occupancy scenarios are considered to show the merits of our predictive framework. This paper aims to address the problem of occupancy estimation with a focus on scenarios where small training data sets are available. By developing robust predictive models that can generalize well with limited data, this research seeks to facilitate the early adoption and practical application of occupancy models in various domains. 

Liouville-Based Predictive Models for Occupancy Estimation using Small Training Data

Finite mixture models of Dirichlet distributions arise in a natural way in several applications involving proportional data. The basic model assumes that the unknown density can be written as a weighted finite sum of Dirichlet distributions, with different mixing weights and different parameters. In this article, on the one hand, we aim to present the finite Dirichlet mixture. On the other hand, we discuss two learning approaches to estimate the parameters of this mixture when dealing with the case of an unknown number of components. We also show the potential of the Dirichlet mixture through a series of experiments involving artificial data and real data that concern the challenging problem of images categorization.


Keywords:

clustering;
Dirichlet distribution;
EM;
mixture model;
variational learning

Recognition and Clustering of Dirichlet Mixtures

Mixture models are a common unsupervised learning technique that have been widely used to statistically approximate and analyse heterogenous data. In this paper, an effective mixture model-based approach for positive vectors clustering and modeling is proposed. Our mixture model is based on the inverted Beta-Liouville (IBL) distribution. To deploy the proposed model, we introduce an entropy-based variational inference algorithm. The performance of the proposed model is evaluated on two real-world applications, namely, human activity recognition and image categorization.

/pdf/entropy-based-variational-learning-of-finite-inverted-beta-4l3csl0e5c.pdf

Wentao Fan

Papers

Spatio-temporal object recognition using variational learning of an infinite statistical model

Liouville-Based Predictive Models for Occupancy Estimation using Small Training Data

Recognition and Clustering of Dirichlet Mixtures

Entropy-based Variational Learning of Finite Inverted Beta-Liouville Mixture Model.

A Generalized Inverted Dirichlet Predictive Model for Activity Recognition Using Small Training Data