Hierarchical fuzzy modeling techniques have great advantage since model accuracy and complexity can be easily controlled thanks to the transparent model structures. A novel tool for regression tree identification is proposed based on the synergistic combination of fuzzy c-regression clustering and the concept of hierarchical modeling. In a special case (c = 2), fuzzy c-regression clustering can...

In this paper, we consider the problem of modeling and analyzing the application communication reliability of wireless sensor networks (WSN) with different topologies, including star, tree, mesh, and hierarchical clustering. We propose reliability measures that integrate the conventional connectivity-based network reliability with the sensing coverage measure of WSN. We apply a reduced ordered ...

We describe a new wavelet transform, for use on hierarchies or binary rooted trees. The theoretical framework of this approach to data analysis is described. Case studies are used to further exemplify this approach. A first set of application studies deals with data array smoothing, or filtering. A second set of application studies relates to hierarchical tree condensation. Finally, a third stu...

Provision of training data sets is one of the core requirements for event-based supervised NILM (NonIntrusive Load Monitoring) algorithms. Due to diversity in appliances’ technologies, in-situ training by users is often required. This process might require continuous user-interaction to ensure that a high quality training data set is provided. Pre-populating a training data set could potentiall...

In this paper we propose a new class of tree machines, called generalized compressed tree machines. Generalized compressed tree machines are obtained from complete binary tree machines by performing certain`con-traction' operations. A generalized compressed tree machine of height h has approximately 25% less number of processors than a complete binary tree of height h. We show that these machin...

Abstract. A hyperplane search tree is a binary tree used to store a set S of n d-dimensional data points. In a random hyperplane search tree for S, the root represents a hyperplane defined by d data points drawn uniformly at random from S. The remaining data points are split by the hyperplane, and the definition is used recursively on each subset. We assume that the data are points in general p...

Histograms are a lossy compression technique widely applied in various application contexts, like query optimization, statistical and temporal databases, OLAP applications, data streams, and so on. In most cases, accuracy in reconstructing from the histogram some original information, plays a crucial role. Thus, several proposals for constructing histograms trying to maximize their accuracy, ha...

We describe a simple new technique for spatio-temporal segmentation of video sequences. Each pixel of a 3D space-time video stack is mapped to a 7D feature point whose coordinates include three color components, two motion angle components and two motion position components. The clustering of these feature points provides color segmentation and motion segmentation, as well as a consistent label...

This paper presents a hierarchical approach to the Large-Scale Speaker Recognition problem. In here the authors present a binary tree data-base approach for arranging the trained speaker models based on a distance measure designed for comparing two sets of distributions. The combination of this hierarchical structure and the distance measure [1] provide the means for conducting a large-scale ve...

This paper deals with the problem of fine-grained image classification and introduces the notion of hierarchical metric learning for the same. It is indeed challenging to categorize fine-grained image classes merely in terms of a single level classifier given the subtle inter-class visual differences. In order to tackle this problem, we propose a two stage framework where i) the image categorie...