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基于逻辑回归函数的加权K-means聚类算法
欧式距离 特征加权的K-means算法 逻辑回归函数 初始聚类中心
2022/3/15
传统K-means聚类算法通过欧式距离计算样本的相似度，将数据所有的属性特征均平等对待，忽略每个属性特征的不同贡献，导致样本相似度计算的准确率不高。针对这个不足，提出一种特征加权的K-means算法进行优化。首先，运用Softmax和Sigmoid逻辑回归函数计算特征权重，使得加权的欧式距离更能准确地表示样本相似度；其次，优化初始聚类中心选择策略，选择距离较大的K个样本作为初始聚类中心，可有效避免...
Tests atternative to higher criticism for high dimensional means under sparsity and column-wise dependence
Large deviation Large p, small n Optimal detection boundary Sparse signal Thresholding Weak dependence
2016/1/25
We consider two alternative tests to the Higher Criticism test of Donoho and Jin (2004) for high dimensional means under the spar-sity of the non-zero means for sub-Gaussian distributed data with unkn...
Recurrence-based time series analysis by means of complex network methods
Chaotic Dynamics (nlin.CD) Social and Information Networks (cs.SI) Data Analysis Statistics and Probability (physics.data-an) Physics and Society (physics.soc-ph)
2010/11/10
Complex networks are an important paradigm of modern complex systems sciences which allows quantitatively assessing the structural properties of systems composed of different interacting entities. Dur...