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基于局部和全局映射函数的流形降维空间球形覆盖分类算法*

DOI: 10.16451/j.cnki.issn1003-6059.201504008, PP. 354-360

Keywords: 覆盖分类,流形,低维子空间,局部和全局映射(LGRM)

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Abstract:

为探索高维数据本质结构和低维表示,并避免一般流形学习中测试数据不能显式降维的不足,提出基于局部和全局映射函数的流形降维空间球形覆盖分类算法.该算法首先抽象融合局部和全局信息映射模型,分别优化局部拉普拉斯矩阵和全局拉普拉斯矩阵,通过对局部和全局拉普拉斯矩阵进行特征值分解,得到训练样本的低维表示.然后借助核映射获取测试样本的低维表示.最后在低维空间建立球形覆盖分类模型,实现目标分类.在MNIST手写体数据集、YaleB和AR人脸数据集上的实验表明文中算法的有效性,证明其在实际应用领域具有一定价值.

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