Abstract
In this paper we propose an algorithm, Simple Hebbian PCA, and prove that it is able to calculate the principal component analysis (PCA) in a distributed fashion across nodes. It simplifies existing network structures by removing intralayer weights, essentially cutting the number of weights that need to be trained in half.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
K. Khanda, D. Salikhov, K. Gusmanov, M. Mazzara, N. Mavridis, Microservice-based iot for smart buildings, in 31st International Conference on Advanced Information Networking and Applications Workshops, AINA 2017 Workshops, Taipei, Taiwan, 27–29 March 2017, pp. 302–308
D. Salikhov, K. Khanda, K. Gusmanov, M. Mazzara, N. Mavridis, Jolie good buildings: Internet of things for smart building infrastructure supporting concurrent apps utilizing distributed microservices, in Selected Papers of the First International Scientific Conference Convergent Cognitive Information Technologies (Convergent 2016), pp. 48–53
T. Soyata, R. Muraleedharan, J. Langdon, C. Funai, S. Ames, M. Kwon, W. Heinzelman, Combat: mobile-cloud-based compute/communications infrastructure for battlefield applications, vol. 8403 (2012), pp. 84030K–84030K–13
C. Kruger, G.P. Hancke, Implementing the internet of things vision in industrial wireless sensor networks, in 2014 12th IEEE International Conference on Industrial Informatics (INDIN) (IEEE, 2014), pp. 627–632
L. Johard, E. Ruffaldi, A connectionist actor-critic algorithm for faster learning and biological plausibility, in 2014 IEEE International Conference on Robotics and Automation, ICRA 2014, Hong Kong, China, 31 May–7 June 2014 (IEEE, 2014), pp. 3903–3909
J. Qiu, H. Wang, J. Lu, B. Zhang, K.-L. Du, Neural network implementations for pca and its extensions. ISRN Artif. Intell. 2012 (2012)
E. Oja, Simplified neuron model as a principal component analyzer. J. Math. Biol. 15(3), 267–273 (1982)
T.D. Sanger, Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural Netw. 2(6), 459–473 (1989)
Y. Bengio, A. Courville, P. Vincent, Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798–1828 (2013)
J. Rubner, P. Tavan, A self-organizing network for principal-component analysis. EPL Europhys. Lett. 10(7), 693 (1989)
S. Kung, K. Diamantaras, A neural network learning algorithm for adaptive principal component extraction (apex), in International Conference on Acoustics, Speech, and Signal Processing (IEEE, 1990), pp. 861–864
C. Pehlevan, T. Hu, D.B. Chklovskii, A hebbian/anti-hebbian neural network for linear subspace learning: a derivation from multidimensional scaling of streaming data, Neural computation (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Johard, L., Rivera, V., Mazzara, M., Lee, J.Y. (2018). Self-adaptive Node-Based PCA Encodings. In: Ciancarini, P., Litvinov, S., Messina, A., Sillitti, A., Succi, G. (eds) Proceedings of 5th International Conference in Software Engineering for Defence Applications. SEDA 2016. Advances in Intelligent Systems and Computing, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-319-70578-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-70578-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70577-4
Online ISBN: 978-3-319-70578-1
eBook Packages: EngineeringEngineering (R0)