Abstract
We present a novel algorithm (Principal Sensitivity Analysis; PSA) to analyze the knowledge of the classifier obtained from supervised machine learning techniques. In particular, we define principal sensitivity map (PSM) as the direction on the input space to which the trained classifier is most sensitive, and use analogously defined \(k\)-th PSM to define a basis for the input space. We train neural networks with artificial data and real data, and apply the algorithm to the obtained supervised classifiers. We then visualize the PSMs to demonstrate the PSA’s ability to decompose the knowledge acquired by the trained classifiers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Deepface: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1701–1708 (2014)
Horikawa, T., Tamaki, M., Miyawaki, Y., Kamitani, Y.: Neural decoding of visual imagery during sleep. Science 340, 639–642 (2013)
Uberbacher, E.C., Mural, R.J.: Locating protein-coding regions in human DNA sequences by a multiple sensor-neural network approach. Proceedings of the National Academy of Sciences 88, 11261–11265 (1991)
Hinton, G.E.: Dark knowledge. Presented as the keynote in BayLearn (2014)
Baehrens, D., Schroeter, T., Harmeling, S., Kawanabe, M., Hansen, K., Muller, K.R.: How to explain individual classification decisions. The Journal of Machine Learning Research 11, 1803–1831 (2010)
Rasmussen, P.M., Madsen, K.H., Lund, T.E., Hansen, L.K.: Visualization of nonlinear kernel models in neuroimaging by sensitivity maps. NeuroImage 55, 1120–1131 (2011)
LaConte, S., Strother, S., Cherkassky, V., Anderson, J., Hu, X.: Support vector machines for temporal classification of block design fMRI data. NeuroImage 26, 317–329 (2005)
Zurada, J.M., Malinowski, A., Cloete, I.: Sensitivity analysis for minimization of input data dimension for feedforward neural network. In: Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), vol. 6, pp. 447–450 (1994)
Zurada, J.M., Malinowski, A., Usui, S.: Perturbation method for deleting redundant inputs of perceptron networks. Neurocomputing 14, 177–193 (1997)
Kjems, U., Hansen, L.K., Anderson, J., Frutiger, S., Muley, S., Sidtis, J., Rottenberg, D., Strother, S.C.: The quantitative evaluation of functional neuroimaging experiments: mutual information learning curves. NeuroImage 15, 772–786 (2002)
Jenatton, R., Obozinski, G., Bach, F.: Structured sparse principal component analysis. In: Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), vol. 9, pp. 366–373 (2010)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine Learning in Python. The Journal of Machine Learning Research 12, 2825–2830 (2012)
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580, 1–18 (2012)
Yukinawa, N., Oba, S., Kato, K., Ishii, S.: Optimal aggregation of binary classifiers for multiclass cancer diagnosis using gene expression profiles. IEEE/ACM Transactions on Computational Biology and Bioinformatics 6, 333–343 (2009)
Kontos, D., Megalooikonomou, V.: Fast and effective characterization for classification and similarity searches of 2D and 3D spatial region data. Pattern Recognition 38, 1831–1846 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Koyamada, S., Koyama, M., Nakae, K., Ishii, S. (2015). Principal Sensitivity Analysis. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9077. Springer, Cham. https://doi.org/10.1007/978-3-319-18038-0_48
Download citation
DOI: https://doi.org/10.1007/978-3-319-18038-0_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18037-3
Online ISBN: 978-3-319-18038-0
eBook Packages: Computer ScienceComputer Science (R0)