Transfer Extreme Learning Machine with Cross Domain Mean Approximation Projection
Shaofei Zang, Pengfei Zhang, Longlong Guo, Jianwei Ma, Yongfa Chang, Chao Ma · 2022
Traditional extreme learning machine (ELM) with the advantages of fast speed and high precision, is widely used in pattern recognition and machine learning. However, it has two shortcomings: 1) Its accuracy decreases greatly under lack of available training samples. 2) The performance is greatly affected by the initialization of hidden-layer parameters. To solve the above two problems, we propose a novelty ELM named transfer extreme learning machine with cross domain mean approximation projection (TELM-CDMAP). It first uses cross domain mean approximation (CDMA) to project input data into feature subspace instead of the data transformation from input-layer to hidden-layer. Then, we apply a between-domain scatter matrix (BDSM) term into the hidden-layer and solve the optimal output weight with the ability of knowledge transfer. The classification experiments on Office+Caltech object recognition and USPS+MNIST digital handwriting datasets are carried out and the result show the effectiveness of our ELM models.