Abstract
In multi-label classification, high-dimensional and sparse binary label vectors usually make existing multi-label classifiers perform unsatisfactorily, which induces a group of label compression coding (LCC) techniques particularly. So far, several linear LCC methods have been introduced via considering linear relations among labels. In this paper, we extend traditional three-layer auto-encoder to construct a five-layer one (i.e., five-layer symmetrical neural network), and then apply the training principle in extreme learning machine to determine all network weights. Therefore, a non-linear LCC approach is proposed to capture non-linear relations of labels, where the first three-layer network is regarded as a encoder and the last two layers act as a decoder. The experimental results on three benchmark data sets show that our proposed method performs better than four existing linear LCC methods according to five performance measures.
This work was supported by the Natural Science Foundation of China (NSFC) under Grant 61273246.
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Luo, J., Cao, L., Xu, J. (2016). A Non-linear Label Compression Coding Method Based on Five-Layer Auto-Encoder for Multi-label Classification. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9949. Springer, Cham. https://doi.org/10.1007/978-3-319-46675-0_45
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