8507685
Global and superlinear convergence of the smoothing Newton method and its application to general box constrained variational inequalities
The smoothing Newton method for solving a system of nonsmooth equations F(x) = 0, which may arise from the nonlinear complementarity problem, the variational inequality problem or other problems, can be regarded as a variant of the smoothing method. At the kth step, the nonsmooth function F is approximated by a smooth function f(.,∈ κ ), and the derivative of f(.,∈ κ ) at x k is used as the Newton iterative matrix. The merits of smoothing methods and smoothing Newton methods are global convergence and convenience in handling. In this paper, we show that the smoothing Newton method is also superlinearly convergent if F is semismooth at the solution and f satisfies a Jacobian consistency property. We show that most common smooth functions, such as the Gabriel-More function, have this property. As an application, we show that for box constrained variational inequalities if the involved function is P- uniform, the iteration sequence generated by the smoothing Newton method will converge to the unique solution of the problem globally and superlinearly (quadratically).
33892015
A Multiple-Criteria Quadratic Programming Approach to Network Intrusion Detection
The early and reliable detection and deterrence of malicious attacks, both from external and internal sources are a crucial issue for today’s e-business. There are various methods available today for intrusion detection; however, every method has its limitations and new approaches should still be explored. The objectives of this study are twofold: one is to discuss the formulation of Multiple Criteria Quadratic Programming (MCQP) approach, and to investigate the applicability of the quadratic classification method to the intrusion detection problem. The demonstration of successful Multiple Criteria Quadratic Programming application in intrusion detection can add another option to network security toolbox. The classification results are examined by cross-validation and improved by an ensemble method. The results demonstrated that MCQP is excellent and stable. Furthermore, the outcome of MCQP can be improved by the ensemble method.
960732
Computing the Assignment of Orthologous Genes via Genome Rearrangement
The assignment of orthologous genes between a pair of genomes is a fundamental and challenging problem in comparative genomics. Existing methods that assign orthologs based on the similarity between DNA or protein sequences may make erroneous assignments when sequence similarity does not clearly delineate the evolutionary relationship among genes of the same families. In this paper, we present a new approach to ortholog assignment that takes into account both sequence similarity and evolutionary events at genome level, where orthologous genes are assumed to correspond to each other in the most parsimonious evolving scenario under genome rearrangement. It is then formulated as a problem of computing the signed reversal distance with duplicates between two genomes of interest, for which an efcient heuristic algorithm was given by introducing two new optimization problems, minimum common partition and maximum cycle decomposition. Following this approach, we have implemented a high-throughput system for assigning orthologs on a genome scale, called SOAR, and tested it on both simulated data and real genome sequence data. Compared to a recent ortholog assignment method based entirely on homology search (called INPARANOID), SOAR shows a marginally better performance in terms of sensitivity on the real data set because it was able to identify several correct orthologous pairs that were missed by INPARANOID. The simulation results demonstrate that SOAR in general performs better than the iterated exemplar algorithm in terms of computing the reversal distance and assigning correct orthologs.
14274462
Region-Based Image Clustering and Retrieval Using Multiple Instance Learning
Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. We propose an approach based on One-Class Support Vector Machine (SVM) to solve MIL problem in the region-based Content Based Image Retrieval (CBIR). This is an area where a huge number of image regions are involved. For the sake of efficiency, we adopt a Genetic Algorithm based clustering method to reduce the search space. Relevance Feedback technique is incorporated to provide progressive guidance to the learning process. Performance is evaluated and the effectiveness of our retrieval algorithm is demonstrated in comparative studies.
13185710
Foveated observation of shape and motion
Robotic navigation and interaction frequently require that the shape and motion of external objects and events be observed. Many interesting events occur at mixed scales. Subtle localized shape and motion often occurs together with long-range movements. One of the chief challenges in recovering these events is to obtain high resolution imagery suitable for resolving small details, while simultaneously increasing the working volume in which recovery is possible. This paper proposes architecture for mixed scale motion recovery. The robust coverage of a large working volume is provided by a wide area of tracking system. This system localizes interesting motions, and guides a separate foveated system of pan tilt cameras to observe the detailed event at high resolution. We demonstrate two applications, foveated structured light scanning and the capture of muscle deformation while walking. Both applications allow subtle detailed recovery that would not be possible using existing single scale systems.
17670948
A General Framework for Biclustering Gene Expression Data
A large number of biclustering methods have been proposed to detect patterns in gene expression data. All these methods try to find some type of biclusters but no one can discover all the types of patterns in the data. Furthermore, researchers have to design new algorithms in order to find new types of biclusters/patterns that interest biologists. In this paper, we propose a novel approach for biclustering that, in general, can be used to discover all computable patterns in gene expression data. The method is based on the theory of Kolmogorov complexity. More precisely, we use Kolmogorov complexity to measure the randomness of submatrices as the merit of biclusters because randomness naturally consists in a lack of regularity, which is a common property of all types of patterns. On the basis of algorithmic probability measure, we develop a Markov Chain Monte Carlo algorithm to search for biclusters. Our method can also be easily extended to solve the problems of conventional clustering and checkerboard type biclustering. The preliminary experiments on simulated as well as real data show that our approach is very versatile and promising.
62599943
Finding nuggets in documents: A machine learning approach
Document keyphrases provide a concise summary of a document's content, offering semantic metadata summarizing a document. They can be used in many applications related to knowledge management and text mining, such as automatic text summarization, development of search engines, document clustering, document classification, thesaurus construction, and browsing interfaces. Because only a small portion of documents have keyphrases assigned by authors, and it is time-consuming and costly to manually assign keyphrases to documents, it is necessary to develop an algorithm to automatically generate keyphrases for documents. This paper describes a Keyphrase Identification Program (KIP), which extracts document keyphrases by using prior positive samples of human identified phrases to assign weights to the candidate keyphrases. The logic of our algorithm is: The more keywords a candidate keyphrase contains and the more significant these keywords are, the more likely this candidate phrase is a keyphrase. KIP's learning function can enrich the glossary database by automatically adding new identified keyphrases to the database. KIP's personalization feature will let the user build a glossary database specifically suitable for the area of hissher interest. The evaluation results show that KIP's performance is better than the systems we compared to and that the learning function is effective. © 2006 Wiley Periodicals, Inc.