Sparse Representation for Tumor Classification Based on Feature Extraction Using Latent Low-Rank Representation

المؤلفون المشاركون

Zheng, Chun-Hou
Gan, Bin
Zhang, Jun
Wang, Hong-Qiang

المصدر

BioMed Research International

العدد

المجلد 2014، العدد 2014 (31 ديسمبر/كانون الأول 2014)، ص ص. 1-7، 7ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2014-02-11

دولة النشر

مصر

عدد الصفحات

7

التخصصات الرئيسية

الطب البشري

الملخص EN

Accurate tumor classification is crucial to the proper treatment of cancer.

To now, sparse representation (SR) has shown its great performance for tumor classification.

This paper conceives a new SR-based method for tumor classification by using gene expression data.

In the proposed method, we firstly use latent low-rank representation for extracting salient features and removing noise from the original samples data.

Then we use sparse representation classifier (SRC) to build tumor classification model.

The experimental results on several real-world data sets show that our method is more efficient and more effective than the previous classification methods including SVM, SRC, and LASSO.

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

Gan, Bin& Zheng, Chun-Hou& Zhang, Jun& Wang, Hong-Qiang. 2014. Sparse Representation for Tumor Classification Based on Feature Extraction Using Latent Low-Rank Representation. BioMed Research International،Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-470865

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

Gan, Bin…[et al.]. Sparse Representation for Tumor Classification Based on Feature Extraction Using Latent Low-Rank Representation. BioMed Research International No. 2014 (2014), pp.1-7.
https://search.emarefa.net/detail/BIM-470865

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

Gan, Bin& Zheng, Chun-Hou& Zhang, Jun& Wang, Hong-Qiang. Sparse Representation for Tumor Classification Based on Feature Extraction Using Latent Low-Rank Representation. BioMed Research International. 2014. Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-470865

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-470865