依据互信息理论提出的互信息匹配识别模型MIM(Mutual Information Matching),能够有效地综合处理语音信号的统计分布特征与时变分布特征,并具有较强的鲁棒性。介绍了运用互信息进行说话人模式匹配的原理,探讨了基于文本的说话人识别中MI...依据互信息理论提出的互信息匹配识别模型MIM(Mutual Information Matching),能够有效地综合处理语音信号的统计分布特征与时变分布特征,并具有较强的鲁棒性。介绍了运用互信息进行说话人模式匹配的原理,探讨了基于文本的说话人识别中MIM模型的应用,通过说话人辨别实验对MIM模型的性能进行了实验分析,并与其它识别模型DTW和GMM进行了比较。对18名男性和12名女性组成的30名说话人进行的识别实验表明, MIM模型的说话人识别性能较好,在采用LPCC特征参数的情况下,平均错误识别率为1.33％。展开更多
An adaptive topology learning approach is proposed to learn the topology of a practical camera network in an unsupervised way. The nodes are modeled by the Gaussian mixture model. The connectivity between nodes is jud...An adaptive topology learning approach is proposed to learn the topology of a practical camera network in an unsupervised way. The nodes are modeled by the Gaussian mixture model. The connectivity between nodes is judged by their cross-correlation function, which is also used to calculate their transition time distribution. The mutual information of the connected node pair is employed for transition probability calculation. A false link eliminating approach is proposed, along with a topology updating strategy to improve the learned topology. A real monitoring system with five disjoint cameras is built for experiments. Comparative results with traditional methods show that the proposed method is more accurate in topology learning and is more robust to environmental changes.展开更多
This paper applied Maximum Entropy (ME) model to Pinyin-To-Character (PTC) conversion in-stead of Hidden Markov Model (HMM) that could not include complicated and long-distance lexical informa-tion. Two ME models were...This paper applied Maximum Entropy (ME) model to Pinyin-To-Character (PTC) conversion in-stead of Hidden Markov Model (HMM) that could not include complicated and long-distance lexical informa-tion. Two ME models were built based on simple and complex templates respectively, and the complex one gave better conversion result. Furthermore, conversion trigger pair of y A → y B cBwas proposed to extract the long-distance constrain feature from the corpus; and then Average Mutual Information (AMI) was used to se-lect conversion trigger pair features which were added to the ME model. The experiment shows that conver-sion error of the ME with conversion trigger pairs is reduced by 4% on a small training corpus, comparing with HMM smoothed by absolute smoothing.展开更多
In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Int...In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Interactive Multiple Model (IMM) estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algorithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effectiveness when compares with traditional MHT.展开更多
Informative proteins are the proteins that play critical functional roles inside cells.They are the fundamental knowledge of translating bioinformatics into clinical practices.Many methods of identifying informative b...Informative proteins are the proteins that play critical functional roles inside cells.They are the fundamental knowledge of translating bioinformatics into clinical practices.Many methods of identifying informative biomarkers have been developed which are heuristic and arbitrary,without considering the dynamics characteristics of biological processes.In this paper,we present a generative model of identifying the informative proteins by systematically analyzing the topological variety of dynamic protein-protein interaction networks(PPINs).In this model,the common representation of multiple PPINs is learned using a deep feature generation model,based on which the original PPINs are rebuilt and the reconstruction errors are analyzed to locate the informative proteins.Experiments were implemented on data of yeast cell cycles and different prostate cancer stages.We analyze the effectiveness of reconstruction by comparing different methods,and the ranking results of informative proteins were also compared with the results from the baseline methods.Our method is able to reveal the critical members in the dynamic progresses which can be further studied to testify the possibilities for biomarker research.展开更多
文摘依据互信息理论提出的互信息匹配识别模型MIM(Mutual Information Matching),能够有效地综合处理语音信号的统计分布特征与时变分布特征,并具有较强的鲁棒性。介绍了运用互信息进行说话人模式匹配的原理,探讨了基于文本的说话人识别中MIM模型的应用,通过说话人辨别实验对MIM模型的性能进行了实验分析,并与其它识别模型DTW和GMM进行了比较。对18名男性和12名女性组成的30名说话人进行的识别实验表明, MIM模型的说话人识别性能较好,在采用LPCC特征参数的情况下,平均错误识别率为1.33％。
基金The National Natural Science Foundation of China(No.60972001)the Science and Technology Plan of Suzhou City(No.SS201223)
文摘An adaptive topology learning approach is proposed to learn the topology of a practical camera network in an unsupervised way. The nodes are modeled by the Gaussian mixture model. The connectivity between nodes is judged by their cross-correlation function, which is also used to calculate their transition time distribution. The mutual information of the connected node pair is employed for transition probability calculation. A false link eliminating approach is proposed, along with a topology updating strategy to improve the learned topology. A real monitoring system with five disjoint cameras is built for experiments. Comparative results with traditional methods show that the proposed method is more accurate in topology learning and is more robust to environmental changes.
基金Supported by the National Natural Science Foundation of China as key program (No.60435020) and The HighTechnology Research and Development Programme of China (2002AA117010-09).
文摘This paper applied Maximum Entropy (ME) model to Pinyin-To-Character (PTC) conversion in-stead of Hidden Markov Model (HMM) that could not include complicated and long-distance lexical informa-tion. Two ME models were built based on simple and complex templates respectively, and the complex one gave better conversion result. Furthermore, conversion trigger pair of y A → y B cBwas proposed to extract the long-distance constrain feature from the corpus; and then Average Mutual Information (AMI) was used to se-lect conversion trigger pair features which were added to the ME model. The experiment shows that conver-sion error of the ME with conversion trigger pairs is reduced by 4% on a small training corpus, comparing with HMM smoothed by absolute smoothing.
基金Supported by the National Natural Science Foundation of China (No. 60772154)the President Foundation of Graduate University of Chinese Academy of Sciences (No. 085102GN00)
文摘In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Interactive Multiple Model (IMM) estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algorithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effectiveness when compares with traditional MHT.
基金supported by National Natural Science Foundation of China(30970780)Ph.D.Programs Foundation of Ministry of Education of China(20091103110005)+4 种基金the Project for the Innovation Team of Beijing,National Natural Science Foundation of China(81370038)the Beijing Natural Science Foundation(7142012)the Science and Technology Project of Beijing Municipal Education Commission(km201410005003)the Rixin Fund of Beijing University of Technology(2013-RX-L04)the Basic Research Fund of Beijing University of Technology
文摘Informative proteins are the proteins that play critical functional roles inside cells.They are the fundamental knowledge of translating bioinformatics into clinical practices.Many methods of identifying informative biomarkers have been developed which are heuristic and arbitrary,without considering the dynamics characteristics of biological processes.In this paper,we present a generative model of identifying the informative proteins by systematically analyzing the topological variety of dynamic protein-protein interaction networks(PPINs).In this model,the common representation of multiple PPINs is learned using a deep feature generation model,based on which the original PPINs are rebuilt and the reconstruction errors are analyzed to locate the informative proteins.Experiments were implemented on data of yeast cell cycles and different prostate cancer stages.We analyze the effectiveness of reconstruction by comparing different methods,and the ranking results of informative proteins were also compared with the results from the baseline methods.Our method is able to reveal the critical members in the dynamic progresses which can be further studied to testify the possibilities for biomarker research.
