@inproceedings{f5eb32415c114eb69ab5ecd1a891d2ff,
title = "Empirical evaluation of whitening and optimization of feature learning",
abstract = "Deep learning and Feature learning emerged new field in machine learning and beat many, state of the arts results in diverse areas. Well established feature learning Sparse- Autoencoder was chosen in our work and tunes their different critical parameters. We have shown that tweaking right parameter can improved results and good features can be obtained. Different Whitening preprocessing techniques and optimization methods were applied on well known data set corel-100 and found out that Cost effective PCA whitening is also same reliable as cost prone other whitening techniques. Different Optimization methods were analyzed and experiments show L-BFGS beat CG as data goes large.",
keywords = "Feature learning, Optimization, Sparseautoencoder, Whitening",
author = "Nouman Qadeer and Xiabi Liu",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 16th UKSim-AMSS International Conference on Computer Modelling and Simulation, UKSim 2014 ; Conference date: 26-03-2014 Through 28-03-2014",
year = "2014",
doi = "10.1109/UKSim.2014.77",
language = "English",
series = "Proceedings - UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, UKSim 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "36--39",
editor = "Jasmy Yunus and Richard Cant and Ismail Saad and David Al-Dabass and Zuwairie Ibrahim and Alessandra Orsoni",
booktitle = "Proceedings - UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, UKSim 2014",
address = "United States",
}