计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 202-205.
郑世秀1,2,潘振宽1,徐知磊1
ZHENG Shi-xiu1,2,PAN Zhen-kuan1,XU Zhi-lei1
摘要: 数据分类是数据挖掘研究的重要内容,随着数据量以及数据维度的增加,对大规模、高维数据的处理成为关键问题。为提高数据分类的准确率,受计算机视觉中图像分割算法的启发,针对经典的Ratio Cut分类模型提出一种基于非局部算子的实现算法。引进拉格朗日乘子,建立新的能量泛函,并采用交替优化的策略来求解该能量泛函。数值实验表明,算法的准确率及计算效率与传统分类方法相比都有较大提高。
中图分类号:
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