Recognition rate is superior to the traditional PCA algorithm. Finally experiments analyze the relationship between neighbor K and the embedding dimension of algorithms SLLE to the recognition rate.
最后实验分析了SLLE算法近邻数K和嵌入维数对识别率的影响，得到了SLLE算法的最优近邻数K和低维嵌入维数。
The small-data method is improved by false nearest neighbor method calculating embedding dimension.
通过用虚假临界点法计算嵌入维数可以使小数据量法更加完善。
In phase space reconstruction of time sequences, the selection of embedding dimension is important.
嵌入维是时间序列相空间重构中的基本参数。
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