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In recent years, matrix completion has attracted a lot of attention. Conventional matrix completions are shallow and ineffective when dealing with complex data structures. Researchers have recently tried to incorporate deep structures into matrix completion; however, considerable challenges still exist. Most matrix completion methods may fail to work effectively in the presence of limited observations. To enhance the generalization of the reconstruction, adversarial methods are proposed that attempt to fool models by providing deceptive input. The aim is to develop an adversarial training algorithm that resists attacks in a deep model, thus at the same time leading to enhancing the generalization. Therefore, in this paper, we propose an elastic adversarial training to design a high-capacity Deep Nonnegative Matrix Factorization (DNMF) model with proper discovery latent structure of the data and enhanced generalization abilities. In other words, the challenges mentioned above are addressed by perturbing the inputs in DNMF with an elastic loss which is intercalated and adapted between Frobenius and ℓ 2 , 1 norms. This model not only dispenses with adversarial DNMF generation but also is robust towards a mixture of multiple attacks to attain improved accuracy. Extensive simulations show that the proposed approach outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• Extracting dominant opinion patterns of frequent favored-unfavored items. • Assigning users to the patterns to detect high confidence relations between them. • An assigned user to some pattern mediates between users of those patterns. • For each user, an impact value is calculated as the role in rating prediction. • Experimental results on four datasets show the proper performance of our method. In this paper, a new collaborative filtering method is proposed based on finding similar users directly and indirectly to overcome sparsity challenge. Moreover, selecting these users through extracting dominant opinion patterns leads to tackling scalability. In this method, frequent opinions between users are extracted to be used as dominant patterns. Then, users corresponding to the same dominant pattern are considered as direct similar users. Direct similar users who have seen more items and have corresponded to more than one pattern are regarded as reference users. Each reference user mediates between users who may have no/few commonly seen items and they are considered as indirect similar users. Utilizing indirect similar user helps the method to predict opinion of query user about items, which have not been seen by any direct similar user before. Clearly, indirect users are selected based on speculation using available information about direct users' preferences. Thus, the effect of indirect users is considered to be stricter than that of direct users on the final prediction step. Experiments conducted on MovieLens small, MovieLens 100 k, MovieLens 1 M, and Jester datasets showed that the proposed method outperforms the previously introduced methods, especially on sparse data. [ABSTRACT FROM AUTHOR]

Unsupervised feature selection (UFS) is a fundamental task in machine learning and data analysis, aimed at identifying a subset of non-redundant and relevant features from a high-dimensional dataset. Embedded methods seamlessly integrate feature selection into model training, resulting in more efficient and interpretable models. Current embedded UFS methods primarily rely on self-representation or pseudo-supervised feature selection approaches to address redundancy and irrelevant feature issues, respectively. Nevertheless, there is currently a lack of research showcasing the fusion of these two approaches. This paper proposes the Orthogonal Encoder-Decoder factorization for unsupervised Feature Selection (OEDFS) model, combining the strengths of self-representation and pseudo-supervised approaches. This method draws inspiration from the self-representation properties of autoencoder architectures and leverages encoder and decoder factorizations to simulate a pseudo-supervised feature selection approach. To further enhance the part-based characteristics of factorization, orthogonality constraints and local structure preservation restrictions are incorporated into the objective function. The optimization process is based on the multiplicative update rule, ensuring efficient convergence. To assess the effectiveness of the proposed method, comprehensive experiments are conducted on 14 datasets and compare the results with eight state-of-the-art methods. The experimental results demonstrate the superior performance of the proposed approach in terms of UFS efficiency. [ABSTRACT FROM AUTHOR]