基于字典学习的混合采样数据分类方法

混合采样数据不仅仅具有不同采样频率数据之间特征集合不同,还有样本数量不一致等特点,传统的分类方法不能直接使用。因此,本文提出一种基于Fisher判别准则字典学习的混合采样数据分类方法以处理采样数据的分类任务。该模型巧妙借助处理多视图数据的分类思想,利用基于Fisher判别准则的字典学习方法,生成的结构化字典的每个原子与数据的类标签相关,同时采用Fisher判别准则使类内散度更小,类间散度更大来约束编码系数矩阵,从而大大提升分类性能。此外,本文针对混合采样数据的样本数量不一致特点,设计了混合采样数据判别分析模型的分类方案。最后实验结果验证了本文方法的有效性。

Classification Method for Mixed Sampling Data Based on Dictionary Learning

Mixed sampling data, whose different features are collected at different sampling frequencies, pervasively exists in the real world. Because the different sampling data set not only has different features, but also has different number of samples, traditional classification methods cannot be used directly. Therefore, this paper proposes a classification method for mixed sampling data based on Fisher discrimination dictionary learning to solve the classification problem of mixed sampling data. Inspired on some classification ideas for multi-view data, the proposed model compares multiple data collected at multiple sampling frequencies to multiple views of multi-view data, and designs a way to learn a sub-dictionary for each class of each view. A structured dictionary whose dictionary atoms have correspondence to the class labels is learned, so that the within-class scatter is less and the between-class scatter is bigger based on the Fisher discrimination criterion. In addition, this paper designs a specific classification scheme for the inconsistent sample size of mixed sampling data. Finally, experimental results demonstrate the effectiveness of the proposed model.