Asymmetric deep autoencoder-like non-negative matrix factorization for multi-view clustering

Recently, deep autoencoder-like non-negative matrix factorization methodologies have achieved impressive performance in multi-view clustering. These methods encode the input data into latent features and decode the latent features to recover the input data, thus obtaining meaningful features. They primarily focus on exploring the complementary information of multi-view data, but overlook that the features reconstructing the input data well may not be discriminative because the quality of the input data is typically not good enough. For example, the input data usually contains noise. To address this issue, we propose an A symmetric D eep A utoencoder-like N on-negative M atrix F actorization for Multi-view Clustering (ADA-NMF). The framework recovers the underlying data from the input data using a deep autoencoder-like non-negative matrix factorization model while leveraging l 1 -norm regularization to explicitly model and mitigate noise. To this end, we devise an asymmetric deep autoencoder-like architecture that decouples the encoder and decoder components, thereby enabling independent optimization of the encoding and decoding processes. This asymmetric design enhances the capacity of the model to discriminatively extract semantic features, facilitating precise reconstruction of the underlying data. Following the semantic features, ADA-NMF further adaptively learns view-specific local similarity graphs and derives a low-rank tensor representation for multi-view data to capture the cross-view consistency and complementary information. Finally, an efficient optimization algorithm is designed to tackle the optimization problem. Our primary contribution to the field of artificial intelligence is the proposal of a novel deep non-negative matrix factorization framework for multi-view clustering, with demonstrated applicability to engineering tasks involving noisy multi-modal data.