In this paper, unlike previous many linear embedding methods, we propose a non-linear embedding method for multi-label classification. The algorithm embeds both instances and labels into the same space, reflecting label-instance relationship, label-label relationship and instance-instance relationship as faithfully as possible, simultaneously. Such an embedding into two-dimensional space is useful for simultaneous visualization of instances and labels. In addition linear and nonlinear mapping methods of a testing instance are also proposed for multi-label classification. The experiments on thirteen benchmark datasets showed that the proposed algorithm can deal with better small-scale problems, especially in the number of instances, compared with the state-of-the-art algorithms.
CITATION STYLE
Kimura, K., Kudo, M., & Sun, L. (2016). Simultaneous nonlinear label-instance embedding for multi-label classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10029 LNCS, pp. 15–25). Springer Verlag. https://doi.org/10.1007/978-3-319-49055-7_2
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