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In this paper, we employ multiple Single-hidden Layer Feedforward Neural Networks for multi-view action recognition. We propose an extension of the Extreme Learning Machine algorithm that is able to exploit multiple action representations and scatter information in the corresponding ELM spaces for the calculation of the networks' parameters and the determination of optimized network combination weights. The proposed algorithm is evaluated by using two state-of-the-art action video representation approaches on five publicly available action recognition databases designed for different application scenarios. Experimental comparison of the proposed approach with three commonly used video representation combination approaches and relating classification schemes illustrates that ELM networks employing a supervised view combination scheme generally outperform those exploiting unsupervised combination approaches, as well as that the exploitation of scatter information in ELM-based neural network training enhances the network's performance. [ABSTRACT FROM AUTHOR]

A strong assumption to derive generalization guarantees in the standard PAC framework is that training (or source) data and test (or target) data are drawn according to the same distribution. Because of the presence of possibly outdated data in the training set, or the use of biased collections, this assumption is often violated in real-world applications leading to different source and target distributions. To go around this problem, a new research area known as Domain Adaptation (DA) has recently been introduced giving rise to many adaptation algorithms and theoretical results in the form of generalization bounds. This paper deals with self-labeling DA whose goal is to iteratively incorporate semi-labeled target data in the learning set to progressively adapt the classifier from the source to the target domain. The contribution of this work is three-fold: First, we provide the minimum and necessary theoretical conditions for a self-labeling DA algorithm to perform an actual domain adaptation. Second, following these theoretical recommendations, we design a new iterative DA algorithm, called GESIDA, able to deal with structured data. This algorithm makes use of the new theory of learning with (ε,γ,τ)-good similarity functions introduced by Balcan et al., which does not require the use of a valid kernel to learn well and allows us to induce sparse models. Finally, we apply our algorithm on a structured image classification task and show that self-labeling domain adaptation is a new original way to deal with scaling and rotation problems. [ABSTRACT FROM AUTHOR]

Sparse representation based classification has led to interesting image recognition results, while the dictionary used for sparse coding plays a key role in it. This paper presents a novel su-pervised structure dictionary learning (SSDL) algorithm to learn a discriminative and block structure dictionary. We associate label information with each dictionary item and make each class-specific sub-dictionary in the whole structured dictionary have good representation ability to the training samples from the associated class. More specifically, we learn a structured dictionary and a multiclass classifier simultaneously. Adding an inhomogeneous representation term to the objective function and considering the independence of the class-specific sub-dic-tionaries improve the discrimination capabilities of the sparse coordinates. An iteratively op-timization method be proposed to solving the new formulation. Experimental results on four face databases demonstrate that our algorithm outperforms recently proposed competing sparse coding methods. [ABSTRACT FROM AUTHOR]

Similarity measurement is crucial for classification. Based on the manifold assumption, many graph-based algorithms were developed. Almost all methods follow the k-rule or ε-rule to construct a graph, and then focus on the algorithms based on the graph. However, the graph may not represent the local structure well, and it does not fully utilize the label information yet. The local structure can be presented by the local density and the distance between the samples and their neighbors. And the graph constructed by the guidance of label information will be better approximate of the relationship of the input data. In this paper, we propose an adaptive semi-supervised graph constructing method. The similarity is learned when constructing the graph. The advantages of the similarity learned by our method include: (1) The similarity is measured along the manifold by constructing a graph; (2) nearby points and points in the same cluster share high similarity; (3) samples from the same class have higher similarity than samples from different classes. Experimental results show that using the proposed similarity for classification task could get better recognition accuracy. [ABSTRACT FROM AUTHOR]

The quantron is a hybrid neuron model related to perceptrons and spiking neurons. The activation of the quantron is determined by the maximum of a sum of input signals, which is difficult to use in classical learning algorithms. Thus, training the quantron to solve classification problems requires heuristic methods such as direct search. In this paper, we present an approximation of the quantron trainable by gradient search. We show this approximation improves the classification performance of direct search solutions. We also compare the quantron and the perceptron's performance in solving the IRIS classification problem. [ABSTRACT FROM AUTHOR]