Abstract
Independent Component Analysis with a soft Reconstruction cost (RICA) has been recently presented to learn highly over-complete sparse features even on unwhitened data. However, RICA failed to consider the geometrical structure of the data space, which has been shown essential for classification problems. To address this problem, we propose a graph regularized ICA model with Reconstruction constraint for image classification, called gRICA. In particular, we construct an affinity graph to encode the geometrical information, and thereby learn a graph regularized over-complete basis which makes sparse representations respect the graph structure. Experiments conducted on several datasets show the effectiveness of gRICA for classification.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Hyvärinen, A., Karhunen, J., Oja, E.: Independent component analysis, vol. 26. Wiley-Interscience (2001)
Hyvärinen, A., Hurri, J., Hoyer, P.: Natural image statistics, vol. 1. Springer (2009)
Le, Q., Ngiam, J., Chen, Z., Chia, D., Koh, P., Ng, A.: Tiled convolutional neural networks. In: NIPS, vol. 23 (2010)
Coates, A., Lee, H., Ng, A.: An analysis of single-layer networks in unsupervised feature learning. In: AISTATS, vol. 1001 (2010)
Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks, vol. 19 (2007)
Hinton, G., Osindero, S., Teh, Y.: A fast learning algorithm for deep belief nets. Neural Computation 18(7) (2006)
Le, Q., Karpenko, A., Ngiam, J., Ng, A.: Ica with reconstruction cost for efficient overcomplete feature learning. In: NIPS (2011)
Cai, D., He, X., Han, J., Huang, T.: Graph regularized nonnegative matrix factorization for data representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(8) (2011)
Zheng, M., Bu, J., Chen, C., Wang, C., Zhang, L., Qiu, G., Cai, D.: Graph regularized sparse coding for image representation. IEEE Transactions on Image Processing 20(5) (2011)
Chung, F.: Spectral graph theory, vol. 92. Amer. Mathematical Society (1997)
Schmidt, M.: minfunc. (2005)
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Computer Vision and Image Understanding 106(1) (2007)
Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: CVPR. IEEE (2009)
Jiang, Z., Lin, Z., Davis, L.: Learning a discriminative dictionary for sparse coding via label consistent k-svd. In: CVPR. IEEE (2011)
Yu, K., Zhang, T.: Improved local coordinate coding using local tangents. In: ICML (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xiao, Y., Zhu, Z., Zhao, Y. (2012). Graph Regularized ICA for Over-Complete Feature Learning. In: Hu, SM., Martin, R.R. (eds) Computational Visual Media. CVM 2012. Lecture Notes in Computer Science, vol 7633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34263-9_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-34263-9_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34262-2
Online ISBN: 978-3-642-34263-9
eBook Packages: Computer ScienceComputer Science (R0)