Detecting and tracking latent factors from temporal data is an important task. Most existing algorithms for latent topic detection such as Nonnegative Matrix Factorization (NMF) have been designed for static data. These algorithms are unable to capture the dynamic nature of temporally changing data streams. In this paper, we put forward an online NMF (ONMF) algorithm to detect latent factors and track their evolution while the data evolve. By leveraging the already detected latent factors and the newly arriving data, the latent factors are automatically and incrementally updated to reflect the change of factors. Furthermore, by imposing orthogonality on the detected latent factors, we can not only guarantee the unique solution of NMF but also alleviate the partial-data problem, which may cause NMF to fail when the data are scarce or the distribution is incomplete. Experiments on both synthesized data and real data validate the efficiency and effectiveness of our ONMF algorithm.

Authors

• Bin Cao The Hong Kong University of Science and Technology
• Dou Shen
• Jian-Tao Sun
• Xuanhui Wang
• Qiang Yang Hong Kong University of Science and Technology, Hong Kong SAR, China
• Zheng Chen Shanghai Jiaotong University

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