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

Online variational learning of generalized Dirichlet mixture models with feature selection

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.

Variational learning for Dirichlet process mixtures of Dirichlet distributions and applications

Cross-modal retrieval has recently drawn much attention in multimedia analysis, and it is still a challenging topic mainly attributes to its heterogeneous nature. In this paper, we propose a flexible supervised collective matrix factorization hashing (FS-CMFH) to efficient cross-modal retrieval. First, we exploit a flexible collective matrix factorization framework to jointly learn the individual latent space of similar semantic with respected to each modality. Meanwhile, the label consistency across different modalities is simultaneously exploited to preserve both intra-modal and inter-modal semantics within these similar latent semantic spaces. Accordingly, these two ingredients are formulated as a joint graph regularization term in an overall objective function, through which the similar hash codes of different modalities in an instance can be discriminatively obtained to flexibly characterize such instance. As a result, these derived hash codes incorporating higher discrimination power are able to improve the cross-modal searching accuracy significantly. The extensive experiments tested on three popular benchmark datasets show that the proposed approach performs favorably compared to the state-of-the-art competing approaches.

Efficient cross-modal retrieval via flexible supervised collective matrix factorization hashing

In this paper, we present an online variational inference algorithm for finite Dirichlet mixture models learning. Online algorithms allow data points to be processed one at a time, which is important for real-time applications, and also where large scale data sets are involved so that batch processing of all data points at once becomes infeasible. By adopting the variational Bayes framework in an online manner, all the involved parameters and the model complexity (i.e. the number of components) of the Dirichlet mixture model can be estimated simultaneously in a closed form. The proposed algorithm is validated through both synthetic data sets and a challenging real-world application namely video background subtraction.

Wentao Fan

Papers

Online variational learning of generalized Dirichlet mixture models with feature selection

Variational learning for Dirichlet process mixtures of Dirichlet distributions and applications

Efficient cross-modal retrieval via flexible supervised collective matrix factorization hashing

Online variational learning of finite Dirichlet mixture models

A hierarchical Dirichlet process mixture of generalized Dirichlet distributions for feature selection