In phase space reconstruction of time series, the selection of embedding dimension is important. Based on the idea of checking the behavior of near neighbors in the reconstruction dimension, a new method to determine ...In phase space reconstruction of time series, the selection of embedding dimension is important. Based on the idea of checking the behavior of near neighbors in the reconstruction dimension, a new method to determine proper minimum embedding dimension is constructed. This method has a sound theoretical basis and can lead to good result. It can indicate the noise level in the data to be reconstructed, and estimate the reconstruction quality. It is applied to speech signal reconstruction and the generic embedding dimension of speech signals is deduced.展开更多
针对直接移除缺失数据的样本可能会导致因样本数量规模的减少从而降低了分类性能的问题,本文基于同时处理缺失数据与构建模式分类模型的策略,提出使用特权信息学习(learning using privileged information,LUPI)的特权最小二乘支持向量...针对直接移除缺失数据的样本可能会导致因样本数量规模的减少从而降低了分类性能的问题,本文基于同时处理缺失数据与构建模式分类模型的策略,提出使用特权信息学习(learning using privileged information,LUPI)的特权最小二乘支持向量机(privileged least squares support vector machine,P-LSSVM),从而达到既能改进其分类性能,又能在保证无偏的情况下确定缺失特征的重要性。本文的基本思想是将完整数据的训练作为特权信息,以此来引导面向整个不完全数据的最小二乘支持向量机(least squares support vector machine,LSSVM)的学习,通过可加性核表达每个特征(含缺失特征)的重要性,推导完整数据的训练的特权信息,并以此构建PLSSVM,运用所提出的留一交叉验证方法完成无偏的缺失特征重要性识别。实验结果表明,本文提出的方法不但在平均测试精度上优于对比算法,还能同时确定缺失特征的重要性。展开更多
基金Supported by the Naltural Science Foundation of Hunan Province(97JJY1006)Open Foundation of Stalte Key Lab. of Theory and Chief Technology on ISN of Xidian University(991894102)
文摘In phase space reconstruction of time series, the selection of embedding dimension is important. Based on the idea of checking the behavior of near neighbors in the reconstruction dimension, a new method to determine proper minimum embedding dimension is constructed. This method has a sound theoretical basis and can lead to good result. It can indicate the noise level in the data to be reconstructed, and estimate the reconstruction quality. It is applied to speech signal reconstruction and the generic embedding dimension of speech signals is deduced.
文摘针对直接移除缺失数据的样本可能会导致因样本数量规模的减少从而降低了分类性能的问题,本文基于同时处理缺失数据与构建模式分类模型的策略,提出使用特权信息学习(learning using privileged information,LUPI)的特权最小二乘支持向量机(privileged least squares support vector machine,P-LSSVM),从而达到既能改进其分类性能,又能在保证无偏的情况下确定缺失特征的重要性。本文的基本思想是将完整数据的训练作为特权信息,以此来引导面向整个不完全数据的最小二乘支持向量机(least squares support vector machine,LSSVM)的学习,通过可加性核表达每个特征(含缺失特征)的重要性,推导完整数据的训练的特权信息,并以此构建PLSSVM,运用所提出的留一交叉验证方法完成无偏的缺失特征重要性识别。实验结果表明,本文提出的方法不但在平均测试精度上优于对比算法,还能同时确定缺失特征的重要性。
