An initial study on the rank of input matrix for extreme learning machine

It is noted that the rank of input data matrix has a critical impact on the performance of a trained classifier model. This paper presents a study on the rank of input data matrix based on a classification model of extreme learning machine which is a single hidden layer feed-forward neural network with non-iterative training. The changing tendency of model accuracy with the increase of input data matrix rank is experimentally investigated and the relationship between the input matrix rank and classification problem complexity is addressed. The analysis and experiments show some meaningful results.