A Sparse Classification Based on a Linear Regression Method for Spectral Recognition

Abstract

This study introduces a spectral-recognition method based on sparse representation. The proposed method, the linear regression sparse classification (LRSC) algorithm, uses different classes of training samples to linearly represent the prediction samples and to further classify them according to residuals in a linear regression model. Two kinds of spectral data with completely different physical properties were used in this study. These included infrared spectral data and laser-induced breakdown spectral (LIBS) data for Tegillarca granosa samples polluted by heavy metals. LRSC algorithm was employed to recognize the two classes of data, and the results were compared with common spectral-recognition algorithms, such as partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), artificial neural network (ANN), random forest (RF), and support vector machine (SVM), in terms of recognition rate and parameter stability. The results show that LRSC algorithm is not only simple and convenient, but it also has a high recognition rate.

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“…In addition to the PCA, the classic partial least squares discriminant analysis (PLS-DA) model was also considered to discriminate the pre- and postexcise group [ 24 ]. Additionally, another two analysis methods of variable importance in projection (VIP) [ 25 ] and continuous wavelet transform (CWT) [ 26 , 27 ] were also introduced to further improve the classifying performance of the calibration model.…”