Abstract
Linear multilayer ICA (LMICA) is an approximate algorithm for independent component analysis (ICA). In LMICA, approximate independent components are efficiently estimated by optimizing only highly-dependent pairs of signals. Recently, a new method named “recursive multidimensional scaling (recursive MDS)” has been proposed for the selection of pairs of highly-dependent signals. In recursive MDS, signals are sorted by one-dimensional MDS at first. Then, the sorted signals are divided into two sections and each of them is sorted by MDS recursively. Because recursive MDS is based on adaptive PCA, it does not need the stepsize control and its global optimality is guaranteed. In this paper, the LMICA algorithm with recursive MDS is applied to large natural scenes. Then, the extracted independent components of large scenes are compared with those of small scenes in the four statistics: the positions, the orientations, the lengths, and the length to width ratios of the generated edge detectors. While there are no distinct differences in the positions and the orientations, the lengths and the length to width ratios of the components from large scenes are greater than those from small ones. In other words, longer and sharper edges are extracted from large natural scenes.
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References
Jutten, C., Herault, J.: Blind separation of sources (part I): An adaptive algorithm based on neuromimetic architecture. Signal Processing 24(1), 1–10 (1991)
Comon, P.: Independent component analysis - a new concept? Signal Processing 36, 287–314 (1994)
Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 7, 1129–1159 (1995)
Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks 10(3), 626–634 (1999)
Cardoso, J.F., Laheld, B.: Equivariant adaptive source separation. IEEE Transactions on Signal Processing 44(12), 3017–3030 (1996)
Matsuda, Y., Yamaguchi, K.: Linear multilayer ICA generating hierarchical edge detectors. Neural Computation 19, 218–230 (2007)
Cardoso, J.F.: High-order contrasts for independent component analysis. Neural Computation 11(1), 157–192 (1999)
Matsuda, Y., Yamaguchi, K.: Linear multilayer ICA with recursive MDS (preprint, 2007)
Cox, T.F., Cox, M.A.A.: Multidimensional scaling. Chapman & Hall, London (1994)
Yang, B.: Projection approximation subspace tracking. IEEE Transactions on Signal Processing 43(1), 95–107 (1995)
van Hateren, J.H., van der Schaaf, A.: Independent component filters of natural images compared with simple cells in primary visual cortex. Proceedings of the Royal Society of London: B 265, 359–366 (1998)
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© 2008 Springer-Verlag Berlin Heidelberg
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Matsuda, Y., Yamaguchi, K. (2008). Extraction of Approximate Independent Components from Large Natural Scenes. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69158-7_66
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DOI: https://doi.org/10.1007/978-3-540-69158-7_66
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