Feature selection embedded subspace clustering with low-rank and locality constraints

Subspace clustering analysis has good performance for the clustering problem of high dimensional data. In recent years, subspace clustering analysis algorithms based on representation has also been widely concerned. But when the feature dimension is too high, it not only increases the time complexity of the operation, but also reduces the performance of the algorithm, so it is an important research topic how to use the less feature dimension to carry on the subspace clustering analysis. In this paper, the feature selection method is added to the algorithm framework of low rank representation, and the two are fused into a new single model, and a new subspace clustering algorithm is proposed by using local constraint conditions. The algorithm selects a small number of related feature dimensions to represent low rank data. This not only reduces the complexity of the algorithm, but also helps to accurately reveal the relationship between the data, because the relationship between the data is not influenced by the unrelated feature dimension through the selection of the features. In addition, locality constraints are used in the learning process. Therefore, the learning process of feature and subspace clustering promotes each other and leads to powerful data representation. A large number of experiments on the real datasets have also proved the effectiveness of this method.