Computational Experience with Pseudoinversion-Based Training of Neural Networks Using Random Projection Matrices

  • Luca Rubini
  • Rossella Cancelliere
  • Patrick Gallinari
  • Andrea Grosso
  • Antonino Raiti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8722)


Recently some novel strategies have been proposed for neural network training that set randomly the weights from input to hidden layer, while weights from hidden to output layer are analytically determined by Moore-Penrose generalised inverse; such non-iterative strategies are appealing since they allow fast learning. Aim of this study is to investigate the performance variability when random projections are used for convenient setting of the input weights: we compare them with state of the art setting i.e. weights randomly chosen according to a continuous uniform distribution. We compare the solutions obtained by different methods testing this approach on some UCI datasets for both regression and classification tasks; this results in a significant performance improvement with respect to conventional method.


random projections weights setting pseudoinverse matrix 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Luca Rubini
    • 1
  • Rossella Cancelliere
    • 1
  • Patrick Gallinari
    • 2
  • Andrea Grosso
    • 1
  • Antonino Raiti
    • 1
  1. 1.Department of Computer ScienceUniversità di TorinoTurinItaly
  2. 2.Laboratory of Computer Sciences, LIP6Université Pierre et Marie CurieParisFrance

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