Publicação

Automatic Identification of Cigarette Brand Using Near-Infrared Spectroscopy and Sparse Representation Classification Algorithm

Detalhes bibliográficos
Resumo:	A cigarette brand automatic classification method using near-infrared (NIR) spectroscopy and sparse representation classification (SRC) algorithm is put forward by the paper. Comparing with the traditional methods, it is more robust to redundancy because it uses non-negative least squares (NNLS) sparse coding instead of principal component analysis (PCA) for dimensionality reduction of the spectral data. The effectiveness of SRC algorithm is compared with PCA-linear discriminant analysis (LDA) and PCA-particle swarm optimization-support vector machine (PSO-SVM) algorithms. The results show that the classification accuracy of the proposed method is higher and is much more efficient.
Autores principais:	Shuangyan,Yang
Outros Autores:	Ying,Hou; Lingchun,Yang; Jianqiang,Zhang; Weijuan,Liu; Changgui,Qiu; Ming,Li; Yanmei,Yang
Assunto:	near-infrared spectroscopy deep learning sparse representation classification non-negative least squares
Ano:	2018
País:	Brasil
Tipo de documento:	artigo
Tipo de acesso:	acesso aberto
Instituição associada:	IBICT
Idioma:	inglês
Origem:	Oasisbr

Descrição
Resumo:	A cigarette brand automatic classification method using near-infrared (NIR) spectroscopy and sparse representation classification (SRC) algorithm is put forward by the paper. Comparing with the traditional methods, it is more robust to redundancy because it uses non-negative least squares (NNLS) sparse coding instead of principal component analysis (PCA) for dimensionality reduction of the spectral data. The effectiveness of SRC algorithm is compared with PCA-linear discriminant analysis (LDA) and PCA-particle swarm optimization-support vector machine (PSO-SVM) algorithms. The results show that the classification accuracy of the proposed method is higher and is much more efficient.

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