对于低维数据的分类很常见,但是对于高维数据的分类却不多,主要是因为维度太高.尤其对于分布不均匀的样本集,传统的局部线性嵌入算法易受到近邻点个数的影响,为了克服这一问题,提出改进距离的局部线性嵌入算法.通过实验表明,改进距离的局部线性嵌入算法能使原来的样本集尽可能的分布均匀,从而降低近邻点个数的取值对局部线性嵌入的影响,在保证分类准确的前提下,达到了有效缩短时间的目的.%For classification of low-dimensional data is very common,but not for the classification of high-dimensional data,mainly because of too high dimension.In particular,for the uneven distribution of the sample set,the traditional locally linear embedding(LLE) algorithm is vulnerable to the impact of the number of nearest neighbor points,In order to overcome this problem,this paper improves locally linear embedding algorithm by changing the distance.Through the experiments indicates that the improved distance locally linear embedding algorithm can make the original sample set distribute evenly as far as possible,thereby reducing the influence of selection of the number of nearest neighbor points on locally linear embedding,on the premise of ensuring accurate classification,to achieve the purpose of effectively shorten the time.
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