A Feature Balance Based Multi-Granularity Deep Network

The remarkable feature extraction capability of deep learning has garnered significant attention. However, with increasing data dimensionality, clustering, as a common data preprocessing method, can transform the high-dimensional feature space into multiple low-dimensional subspaces. A multi-granularity deep network, after being partitioned through clustering, often faces the issue of imbalanced features among different clusters. Therefore, we propose a feature balance strategy in this paper. Through three basic assumptions, minimum feature number, and compression ratio constraints, the final model can learn and represent information in a balanced manner at different levels, thus improving the overall model performance. The experiments show that the proposed strategy can balance the features effectively, enabling the model to achieve higher performance and stability.