关键词: 工业大数据, 高维小样本, 特征提取, 增量线性判别分析, 增量主成分分析, 熵值法

Abstract:

We focus on the failure of Incremental Linear Discriminant Analysis in the case of a high-dimensional small sample in industrial big data. An improved multi-layer incremental feature extraction method for industrial big data is proposed to solve this problem which can reduce dimension effectively, and at the same time the variance information and discriminant information on the sample is kept as much as possible. First, the data streams are updated incrementally with the sliding window in real time, and its outliers are detected and filtered. Second, the incremental principal component analysis is made initially to extract the features of the data and the Fisher discriminant function is used to quantify the classification information contained in each principal element. Then the contribution and recognition ability of principal components are weighted by the entropy evaluation method to select the principal components. The selected principal components constitute a new feature space. Finally, Incremental Linear Discriminant Analysis is made to complete the second feature extraction and classification for the high-dimensional data. Experimental results indicate that the improved method could extract the features effectively in real-time and that the industrial data can be discriminated better.

Key words: industrial big data, high dimensional and small sample, feature extraction, incremental linear discriminant analysis, incremental principal component analysis, entropy method