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International Conference on Intelligent Information Processing (ICIIP)

作者： Zhai, Yikui Liu, Kaipin Piuri, Vincenzo Ying, Zilu Xu, Ying Wuyi Univ Sch & Informat & Engn Jiangmen 529020 Peoples R China Univ Milan Comp Sci Dept I-26013 Crema Italy

ISBN: (纸本)9781450347990

Synthetic aperture radar (SAR) automatic target recognition (ATR) has been receiving more and more attention in the past two decades. A lot of methods have been proposed and studied for radar target recognition. Among some of these methods, they use the supervised algorithms to extracts features. In this paper, we first use a unsupervised algorithm, K-means clustering, which can learn the features without known the class of training samples, for radar target recognition. As the unsupervised algorithm has a high demand on the scale of the data, so we proposed a method of data augmentation to get more data for the unsupervised algorithm, by which the K-means clustering algorithm can learn more unsupervised features. Experimental results on the MSTAR database show that the proposed method can achieve satisfying recognition accuracy compared with other state-of-the-art methods.

关键词： Synthetic aperture radar (SAR) unsupervised algorithm K-means clustering Data augmentation

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Linear Discourse Segmentation of Multi-Party Meetings Based on Local and Global Information

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2015年第11期23卷 1879-1891页

作者： Bokaei, Mohammad Hadi Sameti, Hossein Liu, Yang Sharif Univ Technol Dept Comp Engn Tehran Iran Univ Texas Dallas Dept Comp Sci Richardson TX 75080 USA

Linear segmentation of a meeting conversation is beneficial as a stand-alone system (to organize a meeting and make it easier to access) or as a preprocessing step for many other meeting related tasks. Such segmentation can be done according to two different criteria: topic in which a meeting is segmented according to the different items in its agenda, and function in which the segmentation is done according to the meeting's different events (like discussion, monologue). In this article we concentrate on the function segmentation task and propose new unsupervised methods to segment a meeting into functionally coherent parts. The first proposed method assigns a score to each possible boundary according to its local information and then selects the best ones. The second method uses a dynamic programming approach to find the global best segmentation according to a defined cost function. Since these two methods are complementary of each other, we propose the third method as a combination of the first two ones, which takes advantage of both to improve the final segmentation. In order to evaluate our proposed methods, a subset of a standard meeting dataset (AMI) is manually annotated and used as the test set. Results show that our proposed methods perform significantly better than the previous unsupervised approach according to different evaluation metrics.

关键词： Dynamic programming approach linear discourse segmentation meeting function segmentation unsupervised algorithm

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unsupervised Online Classifier in Sleep Scoring for Sleep Deprivation Studies

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SLEEP 2015年第5期38卷 815-+页

作者： Libourel, Paul-Antoine Corneyllie, Alexandra Luppi, Pierre-Herve Chouvet, Guy Gervasoni, Damien Ctr Rech Neurosci Lyon Lyon France INSERM F-69008 Lyon France CNRS Lyon France Univ Lyon 1 F-69366 Lyon 07 France Inst Natl Sci Appl Lyon France

Study Objective: This study was designed to evaluate an unsupervised adaptive algorithm for real-time detection of sleep and wake states in rodents. Design: We designed a Bayesian classifier that automatically extracts electroencephalogram (EEG) and electromyogram (EMG) features and categorizes non-overlapping 5-s epochs into one of the three major sleep and wake states without any human supervision. This sleep-scoring algorithm is coupled online with a new device to perform selective paradoxical sleep deprivation (PSD). Settings: Controlled laboratory settings for chronic polygraphic sleep recordings and selective PSD. Participants: Ten adult Sprague-Dawley rats instrumented for chronic polysomnographic recordings Measurements: The performance of the algorithm is evaluated by comparison with the score obtained by a human expert reader. Online detection of PS is then validated with a PSD protocol with duration of 72 hours. Results: Our algorithm gave a high concordance with human scoring with an average. coefficient > 70%. Notably, the specificity to detect PS reached 92%. Selective PSD using real-time detection of PS strongly reduced PS amounts, leaving only brief PS bouts necessary for the detection of PS in EEG and EMG signals (4.7 +/- 0.7% over 72 h, versus 8.9 +/- 0.5% in baseline), and was followed by a significant PS rebound (23.3 +/- 3.3% over 150 minutes). Conclusions: Our fully unsupervised data-driven algorithm overcomes some limitations of the other automated methods such as the selection of representative descriptors or threshold settings. When used online and coupled with our sleep deprivation device, it represents a better option for selective PSD than other methods like the tedious gentle handling or the platform method.

关键词： sleep staging automatic scoring unsupervised algorithm paradoxical sleep deprivation

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unsupervised APPROACH TO EXTRACT SUMMARY KEYWORDS IN MEETING DOMAIN 23

UNSUPERVISED APPROACH TO EXTRACT SUMMARY KEYWORDS IN MEETING...

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23rd European Signal Processing Conference (EUSIPCO)

作者： Bokaei, Mohammad Hadi Sameti, Hossein Liu, Yang Sharif Univ Technol Dept Comp Engn Tehran Iran Univ Texas Dallas Dept Comp Sci Dallas TX 75230 USA

ISBN: (纸本)9780992862633

Keywords are words that are used in the reference extracted summary, therefore can be used to discriminate between summary sentences from non-summary ones. Finding these words is important for the extractive summarization algorithms that measure the importance of a sentence based on the importance of its constituent words. This paper is focused on extracting summary keywords in the multi-party meeting domain. We test previously proposed keyword extraction algorithms and evaluate their performance to determine summary keywords. We also propose a new approach which uses discourse information to find local important keywords and show that it outperforms all the previous methods. We evaluate our proposed approach on the standard AMI meeting corpus according to the reference extracted summary prepared in this corpus.

关键词： meeting segmentation funtion segmentation unsupervised algorithm summary keyword extraction

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unsupervised APPROACH TO EXTRACT SUMMARY KEYWORDS IN MEETING DOMAIN

UNSUPERVISED APPROACH TO EXTRACT SUMMARY KEYWORDS IN MEETING...

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European Signal Processing Conference

作者： Mohammad Hadi Bokaei Hossein Sameti Yang Liu Department of Computer Engineering Sharif University of Technology Department of Computer Science The University of Texas at Dallas

ISBN: (纸本)9781479988518

Summary keywords are words that are used in the reference extracted summary, therefore can be used to discriminate between summary sentences from non-summary ones. Finding these words is important for the extractive summarization algorithms that measure the importance of a sentence based on the importance of its constituent words. This paper is focused on extracting summary keywords in the multi-party meeting domain. We test previously proposed keyword extraction algorithms and evaluate their performance to determine summary keywords. We also propose a new approach which uses discourse information to find local important keywords and show that it outperforms all the previous methods. We evaluate our proposed approach on the standard AMI meeting corpus according to the reference extracted summary prepared in this corpus.

关键词： Meeting segmentation Funtion segmentation unsupervised algorithm Summary keyword extraction

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A NEW unsupervised THRESHOLD DETERMINATION FOR HYBRID MODELS

A NEW UNSUPERVISED THRESHOLD DETERMINATION FOR HYBRID MODELS

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Debbabi, Nehla Kratz, Marie Univ Reims CReSTIC Fac Sci Exactes & Nat Reims France Univ Carthage COSIM Engn Coll Commun Tunis Res Lab Tunis Tunisia ESSEC Business Sch CREAR Risk res Ctr Cergy Pontoise France

ISBN: (纸本)9781479928934

A Gauss-GPD hybrid model that links a Gaussian distribution to a Generalized Pareto Distribution (GPD) is considered for asymmetric heavy tailed data. The paper proposes a new unsupervised iterative algorithm to find successively the junction point between the two distributions and to estimate the hybrid model parameters. Simulation results show that this method provides a reliable position for the junction point, as well as an accurate estimation of the GPD parameters, which improves results when compared with other methods. Another advantage of this approach is that it can be adapted to any hybrid model.

关键词： Heavy-tailed data modelling Hybrid density estimation Extreme Value Theory (EVT) Generalized Pareto distribution (GPD) unsupervised algorithm

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unsupervised classification of scattering behaviour using hybrid-polarimetry

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IET RADAR SONAR AND NAVIGATION 2013年第3期7卷 270-276页

作者： Panigrahi, Rajib Kumar Mishra, Amit Kumar Indian Inst Technol Dept Elect & Commun Engn Roorkee 247667 Uttar Pradesh India Univ Cape Town Dept Elect Engn ZA-7701 Rondebosch South Africa

This study presents an unsupervised algorithm for classification of scattering behaviour using hybrid-polarimetric (hybrid-Pol) data. The authors present a maximum likelihood estimation-based unsupervised land cover classification algorithms for hybrid-PolSAR image. This classification technique follows from the m - delta decomposition of hybrid-Pol images. Introduction of a statistical treatment is the major contribution of the current algorithm. Performance of the hybrid-Pol algorithms have been assessed with respect to Freeman-Durden decomposition of fully polarimetric SAR data. The authors have demonstrated, using two different datasets, that proposed algorithm not only gives better overall classification performance, it is also able to classify all the three major types of scattering mechanisms, whereas the existing hybrid-PolSAR classification algorithms mostly fail to classify one of the scattering types.

关键词： image classification maximum likelihood estimation radar imaging radar polarimetry synthetic aperture radar terrain mapping unsupervised classification scattering behaviour hybrid-polarimetry unsupervised algorithm hybrid-polarimetric data hybrid-Pol data maximum likelihood estimation unsupervised land cover classification algorithms hybrid-PolSAR image classification technique hybrid-Pol images statistical treatment hybrid-Pol algorithms Freeman–Durden decomposition fully polarimetric SAR data classification performance scattering mechanisms hybrid-PolSAR classification algorithms scattering types

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unsupervised co-segmentation for 3D shapes using iterative multi-label optimization

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COMPUTER-AIDED DESIGN 2013年第2期45卷 312-320页

作者： Meng, Min Xia, Jiazhi Luo, Jun He, Ying Nanyang Technol Univ Inst Media Innovat BeingThere Ctr Singapore Singapore Nanyang Technol Univ Sch Comp Engn Singapore Singapore Cent S Univ Sch Informat Sci & Engn Changsha Peoples R China

This paper presents an unsupervised algorithm for co-segmentation of a set of 3D shapes of the same family. Taking the over-segmentation results as input, our approach clusters the primitive patches to generate an initial guess. Then, it iteratively builds a statistical model to describe each cluster of parts from the previous estimation, and employs the multi-label optimization to improve the co-segmentation results. In contrast to the existing "one-shot" algorithms, our method is superior in that it can improve the co-segmentation results automatically. The experimental results on the Princeton Segmentation Benchmark demonstrate that our approach is able to co-segment 3D shapes with significant variability and achieves comparable performance to the existing supervised algorithms and better performance than the unsupervised ones. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Co-segmentation unsupervised algorithm Shape correspondence Multi-label optimization

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unsupervised Evolutionary Clustering algorithm for Mixed Type Data

Unsupervised Evolutionary Clustering Algorithm for Mixed Typ...

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2010 IEEE World Congress on Computational Intelligence

作者： Zheng, Zhi Gong, Maoguo Ma, Jingjing Jiao, Licheng Wu, Qiaodi Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Inst Intelligent Informat Proc Xian 710071 Peoples R China

ISBN: (纸本)9781424481262

In this paper, we propose a novel unsupervised evolutionary clustering algorithm for mixed type data, evolutionary k-prototype algorithm (EKP). As a partitional clustering algorithm, k-prototype (KP) algorithm is a well-known one for mixed type data. However, it is sensitive to initialization and converges to local optimum easily. Global searching ability is one of the most important advantages of evolutionary algorithm (EA), so an EA framework is introduced to help KP overcome its flaws. In this study, KP is applied as a local search strategy, and runs under the control of the EA framework. Experiments on synthetic and real-life datasets show that EKP is more robust and generates much better results than KP for mixed type data.

关键词： EA clustering algorithm evolutionary algorithm evolutionary computation global searching ability k-prototype algorithm mixed type data pattern clustering unsupervised algorithm unsupervised learning

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Preliminary results of a Chinese address segmentation algorithm based on self-organizing neural network

Preliminary results of a Chinese address segmentation algori...

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15th International Conference on Geoinformatics

作者： Gao, Duo Li, Qi Peking Univ Inst Remote Sensing & GIS Ctr Spatial Intelligent Comp Beijing 100871 Peoples R China

ISBN: (纸本)9780819469137

In this paper we present preliminary results and lessons learned in an effort to design an automatic Chinese address segmentation algorithm using self-organizing neural network (SONN). The SONN design is composed of an input layer and an output layer, fully connected with random initial weights. Unparsed addresses are transformed into activation patterns on the input layer by activating corresponding neurons simultaneously. The training process is controlled by the Hebbian learning rule and the "k winners take all" (KWTA) competing function. After the self-organization process, each unparsed address will be represented by k (multiple) winners with each winner representing a sub-pattern of the address. The goal of the algorithm is to make these sub-patterns correspond to the word segments of the addresses. Two tests were carried out using a prototype system implementing the algorithm. The results show that the current design can achieve the purpose conditionally and we will discuss the limitations of the SONN for actual applications and the possible improvements of the model will be also discussed.

关键词： Chinese address segmentation Chinese geocoding Chinese natural language processing self-organizing neural network k-winners-take-all algorithm unsupervised algorithm

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