Detect and Track Latent Factors with Online Nonnegative Matrix Factorization

@inproceedings{Cao2007DetectAT,
  title={Detect and Track Latent Factors with Online Nonnegative Matrix Factorization},
  author={Bin Cao and Dou Shen and Jian-Tao Sun and Xuanhui Wang and Qiang Yang and Zheng Chen},
  booktitle={IJCAI},
  year={2007}
}
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… CONTINUE READING
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In Submitted to Computational Statistics and Data Analysis

  • Michael Berry, Murray Browne, Amy Langville, Paul Pauca, Robert Plemmons. Algorithms, applications for approximate nonnegative matrix factorization
  • January
  • 2006
2 Excerpts

3584:474 – 482

  • Philip L.H. Yu Edmond H.C. Wu. Independent component anal Science
  • Aug
  • 2005
1 Excerpt

Independent component analysis for clustering multivariate time series data

  • Philip L. H. Yu Edmond H. C. Wu
  • Lecture Notes in Computer Science
  • 2005
1 Excerpt

In AMOS Technical Conference

  • R. Plemmons J. Piper, P. Pauca, M. Giffin. Object characterization from spectral data factorization, information theory
  • Maui,HI, September
  • 2004
1 Excerpt

Learn

  • Patrik O. Hoyer. Non-negative matrix factorization with s Mach
  • Res., 5:1457–1469,
  • 2004
1 Excerpt

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