Learning and Storing the Parts of Objects: IMF

de Fréin, Ruairí (2014) Learning and Storing the Parts of Objects: IMF. In: IEEE International Workshop on Machine Learning for Signal Processing, September 21-24, 2014, Reims, France.

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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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: @article{deFrein14Learning, author={Ruair\’{i} de Fr\’{e}in$ˆ\dagger$ $ˆ{\dagger\dagger}$}, booktitle={Machine Learning for Signal Processing (MLSP), 2014 IEEE International Workshop on}, title={{Learning and storing the parts of objects: IMF}}, year={2014}, pages={1-6}, keywords={matrix decomposition;signal processing;Barbara;IMF;Peppers;compression ratio;learning algorithm;nonnegative matrix factorization;Approximation methods;Dictionaries;Encoding; Quantization (signal);Signal processing algorithms;Signal to noise ratio;Vectors;compression; matrix factorization}, doi={10.1109/MLSP.2014.6958926}, month={Sept}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6958926&isnumber=6958838},}
Uncontrolled Keywords: matrix factorization; compression; Learning theory and techniques; Data-driven adaptive systems and models; Adaptive algorithms.
Departments or Groups: Walton Institute for Information and Communications Systems Science
Divisions: School of Science
Depositing User: Ruairi De Frein
Date Deposited: 23 Aug 2016 11:27
Last Modified: 23 Aug 2016 11:27
URI: https://repository.wit.ie/id/eprint/3161

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