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Correa-Morris J.,University of Habana | Espinosa-Isidron D.L.,Advanced Technologies Application Center | Alvarez-Nadiozhin D.R.,University of Habana
Pattern Recognition | Year: 2010

In many applied sciences the problem of revealing the underlying (crisp or fuzzy) structure (partitions or covers) in a collection of objects to be represented in non-temporal situations, measures, observations, phenomena, etc., is an essential task. Motivated by the independent use of some different partitions criteria and the theoretical and empirical analysis of some of its properties, in this paper, we introduce an incremental nested partition method which combines these partitions criteria for finding the inner structure of static and dynamic datasets. For this, we proved that there are relationships of nesting between partitions obtained, respectively, from these partition criteria, and besides that the sensitivity when a new object arrives to the dataset is rigorously studied. Our algorithm exploits all of these mathematical properties for obtaining the hierarchy of clusterings. Moreover, we realize a theoretical and experimental comparative study of our method with classical hierarchical clustering methods such as single-link and complete-link and other more recently introduced methods. The experimental results over databases of UCI repository and the AFP and TDT2 news collections show the usefulness and capability of our method to reveal different levels of information hidden in datasets. © 2010 Elsevier Ltd. All rights reserved.


Chai Z.,CAS Institute of Automation | Sun Z.,CAS Institute of Automation | Mendez-Vazquez H.,Advanced Technologies Application Center | He R.,CAS Institute of Automation | Tan T.,CAS Institute of Automation
IEEE Transactions on Information Forensics and Security | Year: 2014

Great progress has been achieved in face recognition in the last three decades. However, it is still challenging to characterize the identity related features in face images. This paper proposes a novel facial feature extraction method named Gabor ordinal measures (GOM), which integrates the distinctiveness of Gabor features and the robustness of ordinal measures as a promising solution to jointly handle inter-person similarity and intra-person variations in face images. In the proposal, different kinds of ordinal measures are derived from magnitude, phase, real, and imaginary components of Gabor images, respectively, and then are jointly encoded as visual primitives in local regions. The statistical distributions of these visual primitives in face image blocks are concatenated into a feature vector and linear discriminant analysis is further used to obtain a compact and discriminative feature representation. Finally, a two-stage cascade learning method and a greedy block selection method are used to train a strong classifier for face recognition. Extensive experiments on publicly available face image databases, such as FERET, AR, and large scale FRGC v2.0, demonstrate state-of-the-art face recognition performance of GOM. © 2005-2012 IEEE.


Vega-Pons S.,Advanced Technologies Application Center | Correa-Morris J.,University of Habana | Ruiz-Shulcloper J.,Advanced Technologies Application Center
Pattern Recognition | Year: 2010

The combination of multiple clustering results (clustering ensemble) has emerged as an important procedure to improve the quality of clustering solutions. In this paper we propose a new cluster ensemble method based on kernel functions, which introduces the Partition Relevance Analysis step. This step has the goal of analyzing the set of partition in the cluster ensemble and extract valuable information that can improve the quality of the combination process. Besides, we propose a new similarity measure between partitions proving that it is a kernel function. A new consensus function is introduced using this similarity measure and based on the idea of finding the median partition. Related to this consensus function, some theoretical results that endorse the suitability of our methods are proven. Finally, we conduct a numerical experimentation to show the behavior of our method on several databases by making a comparison with simple clustering algorithms as well as to other cluster ensemble methods. © 2010 Elsevier Ltd. All rights reserved.


Bande Serrano J.M.,Advanced Technologies Application Center | Palancar J.H.,Advanced Technologies Application Center
Computer Communications | Year: 2012

In this work, we propose a multi-character hardware-based solution using non-deterministic finite automata, NFA, for network intrusion detection. Our approach uses unique subsequence matching. This is a real-time preprocessing phase for detecting the possible presence and the corresponding alignment of the string in the data flow. In doing so, we make a reduction of the area cost for processing multiples characters. Instead of replicating the hardware by splitting the NFAs for each string alignment regarding the block of characters accepted at each cycle, we arrange the NFAs input so they match with the correct string alignment. The architecture is fully pipelined in order to reduce the latency. Taking four characters at the input we achieve multi gigabits throughputs, at the time that thousands of strings can be matched. © 2012 Elsevier B.V. All rights reserved.


Vega-Pons S.,Advanced Technologies Application Center | Ruiz-Shulcloper J.,Advanced Technologies Application Center
International Journal of Pattern Recognition and Artificial Intelligence | Year: 2011

Cluster ensemble has proved to be a good alternative when facing cluster analysis problems. It consists of generating a set of clusterings from the same dataset and combining them into a final clustering. The goal of this combination process is to improve the quality of individual data clusterings. Due to the increasing appearance of new methods, their promising results and the great number of applications, we consider that it is necessary to make a critical analysis of the existing techniques and future projections. This paper presents an overview of clustering ensemble methods that can be very useful for the community of clustering practitioners. The characteristics of several methods are discussed, which may help in the selection of the most appropriate one to solve a problem at hand. We also present a taxonomy of these techniques and illustrate some important applications. © 2011 World Scientific Publishing Company.

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