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Multiobjective genetic algorithm partitioning for hierarchical learning of high-dimensional pattern spaces: A learning-follows-decomposition strategy

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1998年第5期9卷 822-830页

作者： Kumar, R Rockett, P Univ Sheffield Dept Elect & Elect Engn Sheffield S1 3JD S Yorkshire England

In this paper, we present a novel approach to partitioning pattern spaces using a multiobjective genetic algorithm for identifying (near-)optimal subspaces for hierarchical learning. Our approach of "learning-follows-decomposition" is a generic solution to complex high-dimensional problems where the input space is partitioned prior to the hierarchical neural domain instead of by competitive learning, In this technique, clusters are generated on the basis of fitness of purpose-that is, they are explicitly optimized for their subsequent mapping onto the hierarchical classifier. Results of partitioning pattern spaces are presented, This strategy of preprocessing the data and explicitly optimizing the partitions for subsequent mapping onto a hierarchical classifier is found both to reduce the learning complexity and the classification time with no degradation in overall classification error rate. The classification performance of various algorithms is compared and it is suggested that the neural modules are superior for learning the localized decision surfaces of such partitions and offer better generalization.

关键词： genetic algorithms neural-network architecture pattern classification pattern clustering methods

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Uncovering Measurements of Social and Demographic Behavior From Smartphone Location Data

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IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS 2013年第2期43卷 188-198页

作者： Kelly, Daniel Smyth, Barry Caulfield, Brian Univ Coll Dublin Clar Ctr Sensor Web Technol Dublin 4 Ireland

Human behavior, and in particular location behavior, is highly routine based. Modern mobile phones, through global position system (GPS) technology and cell tower and WiFi location identification, enable us to trace human location behavior at scales that were previously unattainable. The goal of this paper is to examine human location behavior, through mobile phone data, and investigate if links can be made between location behavior patterns and particular demographic and social characteristics about an individual. We hypothesize that an individual's daily predictability can be key to linking their behavior to certain characteristics, and we propose predictability and geographic areas of interest models to analyze this hypothesis. Experiments reveal that measurements, which are based on our proposed location predictability models, can correctly infer 17 different characteristics about an individual with an average accuracy of 85.5%.

关键词： Global positioning system (GPS) motion analysis pattern clustering methods pattern recognition

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Microwave Tomography Data Deconstruct of Spatially Diverse C-Band Scatter Components Using clustering Algorithms

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IEEE ACCESS 2022年 10卷 98013-98033页

作者： Sundaram, G. A. Shanmugha Gandhiraj, R. Binoy, B. N. Harun, S., I Surya, S. N. Amrita Vishwa Vidyapeetham Dept Elect & Commun Engn Amrita Sch Engn Coimbatore Tamil Nadu India Amrita Vishwa Vidyapeetham SIERS Res Lab Amrita Sch Engn Coimbatore Tamil Nadu India Nanyang Technol Univ Sch Elect & Elect Engn Singapore 637457 Singapore Delft Univ Technol TU Delft Dept Microelect Microwave Sensing Signals & Syst MS3 Grp NL-2628 Delft Netherlands

Communication signals that propagate through free space are subject to multi-path interference due to scattering by various objects in the propagation channel. The effect is especially severe in complex situations in dense urban environments. To investigate the problem, a typical multi-static detection scenario is reconstructed under controlled laboratory conditions, from which suitable data sets are created. Data-driven models are then employed in EDGE computing platforms to profile the scatter centers based on the subjective manner in which they affect the signals. These have been interpreted primarily based on clustering algorithm (CA) operations- using a select suite of pre-processing models that effectively tame the variations in the C-band spatial-temporal data. A subset of the data of interest could then be subjected to an optional, compute-intensive machine learning (ML) approach. The relative advantages of the proposed method vis-a-vis an array of conventional schemes are highlighted, while also considering its carbon friendly attribute. Given the more significant association of the data to antenna radiation patterns, estimation of the latter can now be performed free of any anechoic chamber set up in a time and cost agnostic manner. The benefit of this work would lie in the realm of mid-band 5G-NR (and the future 6G) cellular communication systems deployment, where optimizing the distributed antenna location attributes on time and cost-constrained scales becomes imperative before any large-scale deployment.

关键词： 5G mobile communication Computational modeling Scattering Artificial intelligence clustering algorithms Radio frequency Distortion Microwave radio communication multi-path channels pattern clustering methods radio propagation terrain factors scattering signal processing antennas

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Fuzzy classification using pattern discovery

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2007年第5期15卷 772-783页

作者： Hamilton-Wright, Andrew Stashuk, Daniel W. Tizhoosh, Hamid R. Univ Guelph Dept Comp & Informat Sci Guelph ON N1G 2W1 Canada Mt Allison Univ Dept Math & Comp Sci Sackville NB E4L 1E6 Canada Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada

Rule-based classifiers allow rationalization of classifications made. This in turn improves understanding which is essential for effective decision support. As a rule based classifier, the pattern Discovery (PD) algorithm functions well in discrete, nominal and continuous data domains. A drawback when using PD as a classifier for decision support is that it has an unbounded decision space that confounds the understanding of the degree of support for a decision. Incorporating PD, into a fuzzy inference system (FIS) allows the the degree of support for a decision to be expressed with intuitively understandable terms. In addition, using discrete algorithms in continuous domains can result in reduced accuracy due to quantization. Fuzzification reduces this "cost of quantization" and improves classification performance. In this work, the PD algorithm was used as a source of rules for a series of FISs implemented using different rule weighting and defuzzification schemes, each providing a linguistic basis for rule description and a [-1... 1] bounded space for expression of decision support. The output of each FIS consists of a suggested outcome, a strong confidence metric describing suggestions within this space and a linguistic expression of the rules. This constitutes a stronger basis for decision making than that provided by PD alone. A variety of synthetic, continuous class distributions with varying degrees of separation was used to evaluate the performance of fuzzy, PD, back-propagation and Bayesian classifiers. Overall, the accuracy of the fuzzy system was found to be similar, but slightly below, that of the inherently continuous valued classifiers and was somewhat improved with respect to the PD classifiers. For the difficult spiral class distributions studied, the fuzzy classifiers were able to make more classifications than the PD classifiers. The correct classification rates for the fuzzy classifiers were similar across the various rule weighting and defuzzific

关键词： decision support systems deductive databases fuzzy logic fuzzy systems pattern classification pattern clustering methods pattern matching statistical databases

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Statistical models for assessing the individuality of fingerprints

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2007年第3期2卷 391-401页

作者： Zhu, Yongfang Dass, Sarat C. Jain, Anil K. Michigan State Univ Dept Stat & Probabil E Lansing MI 48824 USA Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

Following the Daubert ruling in 1993, forensic evidence based on fingerprints was first challenged in the 1999 case of the U.S. versus Byron C. Mitchell and, subsequently, in 20 other cases involving fingerprint evidence. The main concern with the admissibility of fingerprint evidence is the problem of individualization, namely, that the fundamental premise for asserting the uniqueness of fingerprints has not been objectively tested and matching error rates are unknown. In order to assess the error rates, we require quantifying the variability of fingerprint features, namely, minutiae in the target population. A family of finite mixture models has been developed in this paper to represent the distribution of minutiae in fingerprint images, including minutiae clustering tendencies and dependencies in different regions of the fingerprint image domain. A mathematical model that computes the probability of a random correspondence (PRC) is derived based on the mixture models. A PRC of 2.25 x 10(-6) corresponding to 12 minutiae matches was computed for the NIST4 Special Database, when the numbers of query and template minutiae both equal 46. This is also the estimate of the PRC for a target population with a similar composition as that of. NIST4.

关键词： fingerprint identification image processing pattern classification pattern clustering methods pattern matching statistics

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A cluster-based approach for detecting man-made objects and changes in imagery

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2005年第2期43卷 374-387页

作者： Carlotto, MJ Gen Dynam Adv Informat Syst Arlington VA 22209 USA

A new unified approach to object and change detection is presented that involves clustering and analyzing the distribution of pixel values within clusters over one or more images. Cluster-based anomaly detection (CBAD) can detect man-made objects that are: 1) present in a single multiband image;2) appear or disappear between two images acquired at different times;or 3) manifest themselves as spectral differences between two sets of bands acquired at the same time. Based on a Gaussian mixture model, CBAD offers an alternative to compute-intensive, sliding-window algorithms like Reed and Yu's RX-algorithm for single-image object detection. It assumes that background pixel values within clusters can be modeled as Gaussian distributions about mean values that vary cluster-to-cluster and that anomalies (man-made objects) have values that deviate significantly from the distribution of the cluster. This model is valid in situations where the frequency of occurrence of man-made objects is low compared to the background so that they do not form distinct clusters, but are instead split up among multiple background clusters. CBAD estimates background statistics over clusters, not sliding windows, and so can detect objects of any size or shape. This provides the flexibility of filtering detections at the object level. Examples show the ability to detect small compact objects such as vehicles as well as large, spatially extended features (e.g., built-up and bomb-damaged areas). Unlike previous approaches to change detection, which compare pixels, vectors, features, or objects, cluster-based change detection involves no direct comparison of images. In fact, it is identical to the object detection algorithm, different only in the way it is applied. Preliminary results show cluster-based change detection is less sensitive to image misregistration errors than global change detection. The same cluster-based algorithm can also be used for cross-spectral anomaly detection. An example s

关键词： change detection Gaussian processes image analysis image representations image sequence analysis object detection pattern clustering methods vector quantization

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Microwave-Assisted Magnetization Reversal in Individual Isolated Clusters of Cobalt

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IEEE TRANSACTIONS ON MAGNETICS 2008年第11期44卷 2812-2815页

作者： Raufast, Cecile Tamion, Alexandre Bernstein, Estela Dupuis, Veronique Tournier, Thierry Crozes, Thierry Bonet, Edgar Wernsdorfer, Wolfgang Univ Lyon 1 Lab Phys Mat Condensee & Nanostruct F-69622 Villeurbanne France CNRS Inst Neel F-38042 Grenoble France

At the nanometric range, the superparamagnetic state has become a crucial issue in fundamental research as well as practical applications. In both cases, technological improvements require understanding dynamical magnetization reversal processes at the nanosecond time scales. In this paper, we present the first magnetization reversal measurements performed on a single cobalt cluster (counting only 1000 spins) using the micro-SQUID technique by applying a constant magnetic field combined with a radio-frequency field pulse. First of all, we present the different technical steps necessary to detect the magnetic reversals at low temperature (T = 35 mK) of a well-defined nanoparticle prepared by low-energy clusters beam deposition. We previously showed that the 3-D switching Stoner-Wohlfarth astroid represents the magnetic anisotropy of the nanoparticle. Then, an improved device, coupled with a gold stripe line, allow us to reverse such a macrospin using an RF pulse. A qualitative understanding of the magnetization reversal by nonlinear resonance has been obtained with the Landau-Lifschitz-Gilbert equation.

关键词： Dynamic response electron beam lithography pattern clustering methods superconducting quantum interference device (SQUID) magnetometers

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Three-Dimensional LiDAR Data Classifying to Extract Road Point in Urban Area

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2008年第4期5卷 725-729页

作者： Choi, Yun-Woong Jang, Young-Woon Lee, Hyo-Jong Cho, Gi-Sung Chonbuk Natl Univ Res Ctr Ind Technol Jeonju Si 561756 South Korea

The Light Detection and Ranging (LiDAR) system is one of the best ways to accurately and effectively gather 3-D terrain information. However, it is complicated to process the LiDAR cloud data due to its irregularity and large number of collected data points. This letter proposes a novel method to automatically extract urban road network from 3-D LiDAR data. This method uses height and reflectance of LiDAR data, and clustered road point information. Geometric information of general roads is also applied to correctly extract road points group. The proposed method has been tested on various urban areas which contain complicated road networks. The results demonstrate that the integration of height, reflectance, and geometric information of roads is a crucial factor that distinguishes the proposed method in its ability to reliably and correctly classify road points.

关键词： Classification filtering Light Detection and Ranging (LiDAR) data object detection pattern clustering methods

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Fuzzy self-organizing hybrid neural network for gas analysis system

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2000年第2期49卷 424-428页

作者： Osowski, S Brudzewski, K Warsaw Univ Technol Inst Theory Elect Engn & Elect Measurements Warsaw Poland Warsaw Univ Technol Dept Chem Warsaw Poland

The paper presents the gas analysis system applying the self-organizing fuzzy hybrid neural network. The network is composed of the self-organizing competitive fuzzy layer and the supervised multilayer perceptron (MLP) subnetwork, connected in cascade. The characteristic features of this network structure for gas analysis systems are discussed and the results of experiments compared to standard neural solutions based on MLP or classical hybrid network employing the Kohonen layer.

关键词： detectors fuzzy systems neural networks pattern clustering methods signal processing

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FOREGROUND STABILIZATION OF IMAGE SEQUENCES

FOREGROUND STABILIZATION OF IMAGE SEQUENCES

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IEEE International Conference on Multimedia and Expo (ICME 2008)

作者： Yang, Shih-Hsuan Huang, Pei-Cheng Natl Taipei Univ Technol Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781424425709

Hand jitters result in unintentional fluctuation of image sequences taken by hand-held video cameras. Stabilization of the foreground object of interest in pictures is essential for good visual quality. In this paper, foreground stabilization algorithms of image sequences are proposed and evaluated. After performing the block-based motion estimation, grouping techniques are used to identify the foreground motion. The tested grouping techniques include the iterative centroid of foreground, the k-means clustering, and the LMedS (Least Median of Squares) algorithms. An adaptive IIR filtering technique is subsequently applied for motion correction. Compared to conventional stabilization techniques without foreground separation, the proposed methods substantially improve the visual quality of image sequences both subjectively and objectively.

关键词： Image motion analysis pattern clustering methods video cameras video signal processing

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