计算机科学 ›› 2016, Vol. 43 ›› Issue (1): 282-285.doi: 10.11896/j.issn.1002-137X.2016.01.060
徐洁
XU Jie
摘要： 补丁校准是一类有效的维数简约框架。基于补丁校准框架,提出一种最大模糊边界投影算法。该算法引入模糊集理论,从样本的相似度出发,利用非负最小二乘法获取相似近邻,进而构造相似隶属度矩阵,依据相似隶属度矩阵重新定义了模糊边界补丁中心和模糊相似权重。模糊边界补丁中心能很好地降低(或消除)重叠(离群)样本对于特征提取的影响;而模糊相似权重明确了该样本对特征提取所做的贡献。在补丁校准框架下,同类样本间由光照、表情等变化所引起的差异能得到有效的压制,同时不同类样本间距离得以增大,有助于分类性能的提高。在UCI Wine、Yale和Yale-B数据库上的实验验证了所提方法的有效性。
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