Research on Image Data Clustering Algorithm Based on Low Rank Subspace Clustering
Dan Li, Lei Chen, Kailiang Zhang, Chuangeng Tian · 2019
At present, the scale and types of data collected by people have shown explosive growth. It is very difficult to obtain specific and effective classification labels for high-dimensional data. By using subspace clustering method with low rank representation, the linear representation matrix of the data with the lowest rank is found, and the global structure of the original data is preserved to achieve the purpose of optimizing clustering. By comparing K-means clustering, LRR clustering and the improved LRR clustering method of self-adapting graph regularization low rank representation, the experiment proves that the latter has better effect in clustering image data collected from different angles.