Optimizing Extreme Learning Machine via Generalized Hebbian Learning and Intrinsic Plasticity Learning

Traditional extreme learning machine (ELM) has random weights between input layer and hidden layer, this kind of random feature mapping brings non-discriminative feature space and unstable classification accuracy, which greatly limits the performance of the ELM networks. Therefore, to get the well-pleasing input weights, two biologically inspired, unsupervised learning methods were introduced to optimize the traditional ELM networks, namely the generalized hebbian algorithm (GHA) and intrinsic plasticity learning (IPL). The GHA is able to extract the principal components of the input data of arbitrary size, while the IPL tunes the probability density of the neuron’s output towards a desired distribution such as exponential distribution or weber distribution, thereby maximizing the networks information transmission. With the incorporation of the GHA and IPL approach, the optimized ELM networks generates a discriminative feature space and preserves much more characteristic of the input data, accordingly, achieving a better task performance. Based on the above two unsupervised methods, a simple, yet effective hierarchical feature mapping extreme learning machine (HFMELM) is further proposed. With almost no information loss in the layer-wise feature mapping process, the HFMELM is able to learn the high-level representation of the input data. To evaluate the effectiveness of the proposed methods, extensive experiments on several datasets are presented, the results show that the proposed methods significantly outperform the traditional ELM networks.