摘要翻译:
开发针对给定用户查询的快速有效的对象检索算法是一个活跃的研究领域。本研究探讨从音素资料库中撷取时间序列物件至特定使用者模式或查询。该方法利用K-均值聚类对多维相空间进行划分,将一维时间序列检索问题映射为序列检索问题。考虑了整个序列和子序列的匹配问题。研究了该方法对受加性高斯白噪声影响的音素时间序列的鲁棒性。讨论了经典功率谱技术在时间序列反演中的不足。
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英文标题:
《Information retrieval from a phoneme time series database》
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作者:
Radhakrishnan Nagarajan (UAMS), Anand Nagarajan (Symbram LLC),
Mariofanna Milanova (UALR)
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最新提交年份:
2007
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分类信息:
一级分类:Quantitative Biology 数量生物学
二级分类:Quantitative Methods 定量方法
分类描述:All experimental, numerical, statistical and mathematical contributions of value to biology
对生物学价值的所有实验、数值、统计和数学贡献
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一级分类:Quantitative Biology 数量生物学
二级分类:Other Quantitative Biology 其他定量生物学
分类描述:Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要:
Developing fast and efficient algorithms for retrieval of objects to a given user query is an area of active research. The present study investigates retrieval of time series objects from a phoneme database to a given user pattern or query. The proposed method maps the one-dimensional time series retrieval into a sequence retrieval problem by partitioning the multi-dimensional phase-space using k-means clustering. The problem of whole sequence as well as subsequence matching is considered. Robustness of the proposed technique is investigated on phoneme time series corrupted with additive white Gaussian noise. The shortcoming of classical power-spectral techniques for time series retrieval is also discussed.
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PDF链接:
https://arxiv.org/pdf/0712.4275