Sparse support vector machine for pattern recognition

作者：Chen Guangyi; Bui Tien D; Krzyzak Adam^*

来源：Concurrency and Computation: Practice and Experience (CCPE) , 2016, 28(7): 2261-2273.

DOI：10.1002/cpe.3492

• sparse support vector machines
• pattern recognition
• image processing
• machine learning

Support vector machine (SVM) is one of the most popular classification techniques in pattern recognition community. However, because of outliers in the training samples, SVM tends to perform poorly under such circumstances. In this paper, we borrow the idea from compressive sensing by introducing an extra term to the objective function of the standard SVM in order to achieve a sparse representation. Furthermore, instead of using the l(0) norm, we adopt the l(1) norm in our sparse SVM. In most cases, our method achieves higher classification rates than the standard SVM because of sparser support vectors and is more robust to outliers in the datasets. Experimental results show that our proposed SVM is efficient in pattern recognition applications.

• 出版日期2016-5