Incremental Sparse Density-Weighted Twin Support Vector Regression
The Density-Weighted Twin Support Vector Regression(DWTSVR)is a regression algorithm that reflects the internal distribution of data with high prediction accuracy and robustness.However,DWTSVR is unsuitable when training samples are provided in incremental form.To address this problem,this paper proposes the Incremental Sparse DWTSVR(ISDWTSVR).First,to reduce the impact of abnormal samples on the generalization performance of the model,whether the new data is an abnormal sample is identified,and appropriate weights are assigned to valid samples.Next,combining the ideas of matrix dimensionality reduction and principal component analysis,a set of feature column vector bases in the original kernel matrix is screened to replace the original features,achieving sparsity of the kernel matrix and obtaining sparse solutions.Then,using the Newton iteration method and incremental learning strategy,the model information of the previous moment is adjusted to achieve an incremental update of the model.Additionally,the matrix inverse lemma is utilized to avoid solving the inverse matrix directly during the process of incremental updating,further accelerating the training.Finally,simulation experiments are performed using ISDWTSVR on UCI benchmark datasets,and the results are compared with those of existing representative algorithms.Experimental results show that ISDWTSVR inherits the generalization performance of DWTSVR.Upon adding a new sample to Bike-Sharing,a large-scale dataset,the average CPU time for model update is 5.13 s,which is 97.94%shorter than that of DWTSVR.Thus,ISDWTSVR effectively addresses the problem of model retraining from scratch and is suitable for online learning of large-scale datasets.
Twin Support Vector Regression(TSVR)incremental learningsparsificationdensity-weightedNewton iteration method
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