Abstract
In this paper, we propose a novel algorithm, Iterative Semi-supervised Sparse Coding(ISSC), which combines sparse coding and graph-based semi-supervised learning into a unified framework, in order to learn the discriminative sparse codes as well as an effective classification function. The ISSC algorithm fully exploits initial labels and the subsequently predicted labels for sparse codes learning. At the same time, during the graph-based semi-supervised learning stage, the similarity matrix is firstly adjusted through the latest learned sparse codes, and then is utilized to obtain a better classification function. In particular, by solving quadratic optimization, the ISSC approach can give rise to closed-form solution for learned sparse codes. We have extensively evaluated the proposed ISSC method over the widely used datasets for image classification task. The experimental results in terms of classification accuracy demonstrate that the proposed ISSC method is robust and can also achieve significant performance improvements with respect to the state-of-the-arts.
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Zheng, H., Ip, H.H.S. (2013). Image Classification by Iterative Semi-Supervised Sparse Coding. In: Huet, B., Ngo, CW., Tang, J., Zhou, ZH., Hauptmann, A.G., Yan, S. (eds) Advances in Multimedia Information Processing – PCM 2013. PCM 2013. Lecture Notes in Computer Science, vol 8294. Springer, Cham. https://doi.org/10.1007/978-3-319-03731-8_45
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DOI: https://doi.org/10.1007/978-3-319-03731-8_45
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