In this paper, we prove that a real tensor is strictly semi-positive if and only if the corresponding tensor complementarity problem has a unique solution for any nonnegative vector and a real tensor is semi-positive if and only if the corresponding tensor complementarity problem has a unique solution for any positive vector. It is showed that a real symmetric tensor is a (strictly) semi-positi...

We consider the class of semi-infinite programming problems which became in recent years a powerful tool for the mathematical modelling of many real-life problems. In this paper, we study an augmented Lagrangian approach to semi-infinite problems and present necessary and sufficient conditions for the existence of corresponding augmented Lagrange multipliers. Furthermore, we discuss two particu...

We present a semi-supervised incremental learning algorithm for evolving visual data in order to develop a robust and flexible track classification system in a multi camera surveillance scenario. Most existing methods, which are variations on static learning schemes, can not cope with many real-life challenges. The scarcity of labelled data in real applications ends up generating poor classifie...

This work presents a detailed analysis and discussion of a new object tracking method using semi-supervised on-line boosting1. In order to avoid the drifting problem, which presents a challenge to adaptive tracking systems, the new approach incorporates prior knowledge of the tracked object into the tracking process via semi-supervised learning. This method makes it possible to distinguish betw...

8 This article presents a novel ‘self-training’ based semi-supervised classifica9 tion algorithm using the property of aggregation pheromone found in real 10 ants. The proposed method has no assumption regarding the data distribu11 tion and is free from parameters to be set by the user. It can also capture 12 arbitrary shapes of the classes. The proposed algorithm is evaluated with 13 a number ...

Semi-supervised classification methods make use of the large amounts of relatively inexpensive available unlabeled data along with the small amount of labeled data to improve the accuracy of the classification. This article presents a novel ‘self-training’ based semi-supervised classification algorithm using the property of aggregation pheromone found in natural behavior of real ants. The propo...

With the rapid increase of complex and highdimensional sparse data, demands for new methods to select features by exploiting both labeled and unlabeled data have increased. Least regression based feature selection methods usually learn a projection matrix and evaluate the importances of features using the projection matrix, which is lack of theoretical explanation. Moreover, these methods canno...

There is always an asymmetric phenomenon between traffic data quantity and unit information content. Labeled data is more effective but scarce, while unlabeled data is large but weaker in sample information. In an urban transportation assessment system, semi-supervised extreme learning machine (SSELM) can unite manual observed data and extensively collected data cooperatively to build connectio...

This paper presents a semi-supervised methodology for automatic recognition and classification of elderly activity in a cluttered real home environment. The proposed mechanism recognizes elderly activities by using a semantic model of the scene under visual surveillance. We also illustrate the use of trajectory data for unsupervised learning of this scene context model. The model learning proce...