Affiliations: [a] Computer Engineering Technical College, Guangdong Polytechnic of Science And Technology, Zhuhai, Guangdong, China | [b] Department of Anatomy, Division of Basic Medicine, YongZhou Vocational Technical College, Yongzhou, Hunan, China | [c] School of Economics, Jinan University, Guangzhou, Guangdong, China
Correspondence:
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Corresponding author: Yunchuan Jiang, Department of Anatomy, Division of Basic Medicine, YongZhou Vocational Technical College, Yongzhou, Hunan 425100, China. E-mail: [email protected].
Abstract: In order to solve the problem of low accuracy of time dimension feature extraction and classification of high-dimensional large data streams, this paper proposes a time dimension feature extraction and classification algorithm of high-dimensional large data streams based on unsupervised learning. Analyze the trend of high-dimensional data flow changes under machine learning, and achieve dimensionality reduction of high-dimensional large traffic time dimensional data through local save projection. Analyze the spatial relationship between feature attributes and feature space, segment and fit high-dimensional big data streams and time dimensional feature data streams, further segment time dimensional sequences using sliding windows, and complete feature extraction through discrete dyadic wavelet transform. According to the clustering algorithm, cluster the time dimension feature data stream, calculate the cosine similarity of the feature data, model the time dimension feature stream of training samples, use the feature classification function to minimize the classification loss, and use unsupervised learning to achieve the final classification task. The test results show that this method can improve the temporal feature extraction and classification accuracy streams.
Keywords: Unsupervised learning, high dimensional big data flow, time dimension characteristics, low dimensional space, sliding window, discrete dyadic wavelet transform
