Generalized low-rank approximations of matrices revisited

  • Authors:

  • Jun Liu;Songcan Chen;Zhi-Hua Zhou;Xiaoyang Tan

  • Affiliations:

  • Biodesign Institute and Department of Computer Science and Engineering, Arizona State University, Tempe, AZ and Department of Computer Science and Engineering, Nanjing University of Aeronautics an ...;Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China;National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China

  • Venue:
  • IEEE Transactions on Neural Networks
  • Year:
  • 2010

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Abstract

Compared to singular value decomposition (SVD), generalized low-rank approximations of matrices (GLRAM) can consume less computation time, obtain higher compression ratio, and yield competitive classification performance. GLRAM has been successfully applied to applications such as image compression and retrieval, and quite a few extensions have been successively proposed. However, in literature, some basic properties and crucial problems with regard to GLRAM have not been explored or solved yet. For this sake, we revisit GLRAM in this paper. First, we reveal such a close relationship between GLRAM and SVD that GLRAM's objective function is identical to SVD's objective function except the imposed constraints. Second, we derive a lower bound of GLRAM's objective function, and discuss when the lower bound can be touched. Moreover, from the viewpoint of minimizing the lower bound, we answer one open problem raised by Ye (Machine Learning, 2005), i.e., a theoretical justification of the experimental phenomenon that, under given number of reduced dimension, the lowest reconstruction error is obtained when the left and right transformations have equal number of columns. Third, we explore when and why GLRAM can perform well in terms of compression, which is a fundamental problem concerning the usability of GLRAM.