SRAGL-AWCL: A two-step multi-view clustering via sparse representation and adaptive weighted cooperative learning

Sparse representation and cooperative learning are two representative technologies in the field of multi-view spectral clustering. The former can effectively extract features multiple views by removal redundant information contained each view. latter incorporate diversity However, traditional sparse algorithms inadequate preserving internal geometric data manifold regularization. In fact, general approaches rarely consider similarities between graph structures individual views. Moreover, to achieve optimal global feature learning, we present a novel two-step clustering strategy, which combines proposed adaptive with weighted learning. first step, matrix factorization regularization strengthen discrimination samples Specifically, synchronization optimization method introducing better retain its complete structure This ensures correlation through usage similarity matrix. second is performed on view get optimized order ensure that associated various features, also Experiment results several datasets single-view show significantly outperformed state-of-the-art algorithms.