Abstract
BackgroundAs a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Various ELM-based methods for different fields have been proposed. However, the robustness to noise and outliers is always the main problem affecting the performance of ELM.ResultsIn this paper, an integrated method named correntropy induced loss based sparse robust graph regularized extreme learning machine (CSRGELM) is proposed. The introduction of correntropy induced loss improves the robustness of ELM and weakens the negative effects of noise and outliers. By using the L2,1-norm to constrain the output weight matrix, we tend to obtain a sparse output weight matrix to construct a simpler single hidden layer feedforward neural network model. By introducing the graph regularization to preserve the local structural information of the data, the classification performance of the new method is further improved. Besides, we design an iterative optimization method based on the idea of half quadratic optimization to solve the non-convex problem of CSRGELM.ConclusionsThe classification results on the benchmark dataset show that CSRGELM can obtain better classification results compared with other methods. More importantly, we also apply the new method to the classification problems of cancer samples and get a good classification effect.
Highlights
As a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies
CSRGELM is applied to the cancer sample classification tasks of the TCGA integrated datasets
In this paper, we propose a new method named correntropy induced loss based sparse robust graph regularized extreme learning machine (CSRGELM) and apply it to the classification problems of cancer samples
Summary
As a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Universal approximation capability plays a crucial role in settling regression and classification problems Because of this ability, the single hidden layer feedforward neural network has always been the focus and hotspot of researches [1]. Different from traditional neural network models, such as the backpropagation (BP) algorithm [10, 11], the training process of ELM is implemented in one step rather than iteratively [12]. ΒL]T connecting with the hidden layer and the output layer can be determined [14].Since there is no need to iteratively solve the output weight matrix, compared with the traditional backpropagation algorithm, ELM can achieve better generalization performance at a faster speed [2, 3, 7]. Because of the advantages of simple theories, high efficiency, and low manual intervention, ELM has been used as a tool for various applications, such as image classification [15, 16], label learning [17], image quality assessment [18], traffic sign recognition [19], and so on
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