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

Video texture is a new type of medium which can provide a new video with a continuously varying stream of images from a recorded video. It is created by reordering the input video frames in a way which can be played without any visual discontinuity. Recently, a new method of generating video textures has been proposed. It first apply principal components analysis (PCA) to extract signatures or patterns from the original video sequence, and then implement an autoregressive process (AR) model to synthesize new video textures. In this paper, we extend this video texture generation method by comparing PCA with other dimensionality reduction techniques such as probabilistic principal components analysis, kernel principal components analysis, independent component analysis, local linear embedding and Isomap. According to our experiments, these approaches prevail the original approach by providing us video textures with better quality.

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On video textures generation: A comparison between different dimensionality reduction techniques

Online algorithms allow data points to be processed sequentially, which is important for real-time applications. In this paper, we propose a novel online clustering approach based on a mixture of Dirichlet processes with Dirichlet distributions, which can be viewed as an extension of the finite Dirichlet mixture model to the infinite case. Our approach is based on nonparametric Bayesian analysis, the determination of the number of clusters is sidestepped by assuming an infinite number of clusters. By learning the proposed model in an online manner with a variational learning framework, all the involved parameters can be estimated effectively and efficiently in a closed form without introducing the problem of over fitting. The proposed online infinite mixture model is validated through both synthetic data sets and a challenging real-world application namely unsupervised image categorization.

Online Variational Learning for a Dirichlet Process Mixture of Dirichlet Distributions and its Application

In this paper, we formulate 3D model recognition as a statistical inference problem using a Pitman-Yor process mixture of Beta-Liouville Distributions. The proposed model is learned via a collapsed variational inference approach. Unlike classic variational Bayes, the collapsed approach does not make the non-realistic assumption that the model's parameters are independent from the assignment variables, which leads to better modelling and generalization capabilities. The merits and advantages of the proposed approach are shown via extensive experiments.

A novel 3D model recognition approach using Pitman-Yor process mixtures of Beta-Liouville Distributions

Mixture models play a crucial role in pattern recognition methods based on clustering. In this paper, we propose a finite generalized inverted Dirichlet mixture model with a variational learning method for parameter estimation. The highlight of our model is the component splitting approach which handles the problem of model selection in an incremental fashion within the variational framework. Efficiency of proposed model is tested for image categorization tasks.

Variational Learning for Finite Generalized Inverted Dirichlet Mixture Models with a Component Splitting Approach

We propose a nonparametric Bayesian approach, based on hierarchical Dirichlet processes and generalized Dirichlet distributions, for simultaneous clustering and feature selection. The resulting statistical model is learned within a variational framework that we have developed. The merits of the developed model are shown via extensive simulations and experiments when applied to the challenging problem of images categorization.

Wentao Fan

Papers

On video textures generation: A comparison between different dimensionality reduction techniques

Online Variational Learning for a Dirichlet Process Mixture of Dirichlet Distributions and its Application

A novel 3D model recognition approach using Pitman-Yor process mixtures of Beta-Liouville Distributions

Variational Learning for Finite Generalized Inverted Dirichlet Mixture Models with a Component Splitting Approach

A Hierarchical Infinite Generalized Dirichlet Mixture Model with Feature Selection