A central concern for many learning algorithms is how to efficiently store what the algorithm has learned. An algorithm for the compression of Nonnegative Matrix Factorizations is presented. Compression is achieved by embedding the factorization in an encoding routine. Its performance is investigated using two standard test images, Peppers and Barbara. The compression ratio (18:1) achieved by the proposed Matrix Factorization improves the storage-ability of Nonnegative Matrix Factorizations without significantly degrading accuracy (≈ 1-3dB degradation is introduced). We learn as before, but storage is cheaper.
|Publication status||Published - 2014|
|Event||IEEE International Workshop on Machine Learning for Signal Processing - Reims, France, Reims, France|
Duration: 01 Jan 2014 → …
|Conference||IEEE International Workshop on Machine Learning for Signal Processing|
|Period||01/01/2014 → …|
- Adaptive algorithms.
- Data-driven adaptive systems and models;
- Learning theory and techniques;
- matrix factorization;