Supervised Matrix Factorization with sparseness constraints and fast inference

@article{Thom2011SupervisedMF,
  title={Supervised Matrix Factorization with sparseness constraints and fast inference},
  author={Markus Thom and Roland Schweiger and G{\"u}nther Palm},
  journal={The 2011 International Joint Conference on Neural Networks},
  year={2011},
  pages={973-979}
}
Non-negative Matrix Factorization is a technique for decomposing large data sets into bases and code words, where all entries of the occurring matrices are non-negative. A recently proposed technique also incorporates sparseness constraints, in such a way that the amount of nonzero entries in both bases and code words becomes controllable. This paper extends the Non-negative Matrix Factorization with Sparseness Constraints. First, a modification of the optimization criteria ensures fast… CONTINUE READING

Citations

Publications citing this paper.

References

Publications referenced by this paper.
SHOWING 1-10 OF 26 REFERENCES

Similar Papers

Loading similar papers…