Abstract
Non-negative matrix factorization is a machine learning technique that is used to decompose large data matrices imposing the non-negativity constraints on the factors. This technique has received a significant amount of attention as an important problem with many applications in different areas such as language modeling, text mining, clustering, music transcription, and neurobiology (gene separation). In this paper, we propose a new approach called Collaborative Non-negative Matrix Factorization (\(NMF_{Collab}\)) which is based on the collaboration between several NMF (Non-negative Matrix Factorization) models. Our approach \(NMF_{Collab}\) was validated on variant datasets and the experimental results show the effectiveness of the proposed approach.
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Benlamine, K., Grozavu, N., Bennani, Y., Matei, B. (2019). Collaborative Non-negative Matrix Factorization. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. ICANN 2019. Lecture Notes in Computer Science(), vol 11730. Springer, Cham. https://doi.org/10.1007/978-3-030-30490-4_52
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