Abstract
In this paper, we propose an incremental deep learning network for on-line unsupervised feature extraction. This deep learning network is based on 3 data processing components: (1) cascaded incremental orthogonal component analysis network (IOCANet); (2) binary hashing; and (3) blockwise histograms. In this architecture, IOCANet can process online data and get filters to do convolutions. Binary hashing is used to enhance the nonlinearity of IOCANet and reduce the quantity of the data. Eventually, the data is encoded by blockwise histograms. Experiments demonstrate that the proposed architecture has potential results for on-line unsupervised feature extraction.
Author supplied keywords
Cite
CITATION STYLE
Liang, Y., Yang, Y., Shen, F., Zhao, J., & Zhu, T. (2017). An Incremental Deep Learning Network for On-line Unsupervised Feature Extraction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10635 LNCS, pp. 383–392). Springer Verlag. https://doi.org/10.1007/978-3-319-70096-0_40
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
