Flexible ranking extreme learning machine based on matrix-centering transformation
Shizhao Chen, Kai Chen, Chuanfu Xu, Long Lan · 2018
Existing ranking ELM algorithms bias to imbalanced queries since they equally treat each pairwise error. In this study we propose a flexible ranking ELM method based on matrix-centering transformation to replace the traditional graph Laplacian matrix based methods. Specifically, we introduce a useful query-level normalized loss function and enforce the matrix-centering transformation to it to avoid training a bias model. Fortunately, by this setting, we can also greatly simplify the learning process of ELM because of the symmetry and idempotence of the centering matrix. Based on the proposed framework, three different ranking ELM variants are implemented: (a) a regularized ranking ELM model; (b) an enhanced incremental ranking ELM model; and (c) an online sequential ranking ELM model. Experimental results demonstrate that our proposed ranking ELM algorithms can obtain comparable or better performances than the state-of-the-art ranking algorithms.