Adversarial Input Transfer Learning


Adversarial Input Transfer Learning – Non-negative matrix factorization is a key feature of non-negative matrix factorization, especially when the output matrix is unknown. In this work we propose a new matrix factorization approach based on non-negative factorization (NVF) and its extensions. Unlike traditional NVF, NVF has high regularity bound in the input matrix, but it is expensive to compute a regularizer for the latent matrix. To avoid this, we extend the framework of non-negative factorization to the latent matrix space. We propose an efficient approximate non-negative factorization algorithm, which uses the regularization parameter to increase the regularization error rate (ER). The algorithm is flexible as it requires only a single factorizing variable to be replaced by a constant matrix factorization, and the regularizer parameter can be chosen efficiently by the linear convergence method. We show how our method can be applied to matrix factorization tasks such as sparse matrix classification and multi-class classification, and show the superior performance of our method on both datasets.

The most successful and efficient algorithms in the literature have not seen a major increase in adoption. However, existing methods for learning linear models have limited their application to higher dimensions. Inspired by the high-dimensional domain, we propose a novel linear estimator that can be used to encode and evaluate the nonlinear information contained in high-dimensional variables. We then use the learned estimator to reconstruct the model from the information stored in the high-dimensional variable space. Our estimation method can perform better than the state-of-the-art methods in terms of accuracy and robustness.

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Adversarial Input Transfer Learning

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