计算机科学 ›› 2016, Vol. 43 ›› Issue (3): 72-74, 88.doi: 10.11896/j.issn.1002-137X.2016.03.014

• 第十五届中国机器学习会议 • 上一篇    下一篇

一种基于顺序特性的子空间聚类方法

陈丽萍,郭躬德   

  1. 福建师范大学数学与计算机科学学院 福州350007,福建师范大学数学与计算机科学学院 福州350007
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金:面向软件行为鉴别的事件序列挖掘方法研究(61175123)资助

Subspace Clustering Based on Sequential Feature

CHEN Li-ping and GUO Gong-de   

  • Online:2018-12-01 Published:2018-12-01

摘要: 受到Tierney的序列稀疏子空间聚类方法的启发,提出了一种新的基于顺序特性的子空间聚类方法。该方法先通过提升小波变换处理得到信号的低频信息;然后通过强调相邻样本之间的连续性来设置特殊的惩罚项,并根据噪声的大小自动调节惩罚因子;最后过滤系数矩阵中一些小的干扰系数。在人工合成和实际应用的数据集上的实验结果表明,与当前最具代表性的几种稀疏子空间聚类方法相比,所提方法具有较好的实验效果。

关键词: 稀疏子空间聚类,顺序特性,惩罚因子

Abstract: Inspired by Tierney’s subspace clustering of the sequence data,a novel subspace clustering method based on sequential character was proposed.In the beginning,the lifting wavelet transform is applied to extract low-frequency information of the signal,and then a stronger special penalty term is applied to emphasize the similarity between adjacent samples,in which the penalty factor is automatically adjusted according to the noise.The proposed method performs better compared with the most characteristic sparse subspace clustering methods in experiment carried out on a synthetic data set and some data sets from real-world applications.

Key words: Sparse subspace clustering,Sequence feature,Penalty factor

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