Lec11 - School of Computer Science and Technology...

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Nature Inspired Computation and Applications Laboratory School of Computer Science and Technology University of Science and Technology of China Pattern Recognition Lecture 11 Feature Analysis
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Nature Inspired Computation and Applications Laboratory 主要内容 特征分析的定义与目标 特征选择 Filter method Wrapper method Embedded method 特征提取 主成分分析( Principal Component Analysis Fisher 判别分析( Fisher Discriminant Analysis 非线性特征提取
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Nature Inspired Computation and Applications Laboratory 特征分析 特征选择与特征提取的统称 一般作为用于对数据进行预处理 The three stages of pattern classification 特征分析希望用一个比当前特征集合小的特征集 来表示数据,以获得更高的分类精度、分类速度、 或使分类结果更易于解释。 Original data Preprocessing (Feature Selection/Extraction) Classifier
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Nature Inspired Computation and Applications Laboratory 特征分析 特征分析可提高分类精度,因为 数据可能存在噪声 由于训练样本数总是有限,当特征数逐渐增多时,分 类器的泛化性能会首先增加,然后呈下降趋势(维数 灾难) 特征分析可提高分类速度,因为 降低了对存储空间要求 减少了训练和测试时间 分类结果与特征之间的关系更明显,因此易于解释
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Nature Inspired Computation and Applications Laboratory 举例说明 The above figures are taken from the tutorial “Downsizing Data for High Performance in Learning-Introduction to Feature Selection Methods”, given by H. Liu and R. Stine at AAAI-2005 Gene Expression Microarray Analysis
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Nature Inspired Computation and Applications Laboratory 特征选择 假定有 N 个模式,每个模式由 d 个特征表示,要求 选出 l 个特征( l<d 寻求该特征子集的过程,就是求解一个组合优化
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Lec11 - School of Computer Science and Technology...

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