Data Representation with Robust Hypergraph Coding

This paper introduces a novel matrix factorization framework, Data Representation with Robust Hypergraph Coding (MFRHL), designed to enhance unsupervised learning techniques in data mining and machine learning. Traditional matrix factorization methods often fail to capture complex semantic information in datasets. We leverage hypergraph-based Laplacian Eigenmaps to construct a discriminative basis that captures advanced semantic relationships, followed by sparse coding to optimize this basis. The MFRHL framework significantly improves performance in data clustering tasks by effectively handling high-dimensional data and preserving intricate data relationships. Comprehensive experimental results demonstrate that this method consistently outperforms other cutting-edge matrix factorization methods in data clustering.