Abstract：
:Breast tissue classification is an important and effective way for computer aided diagnosis of breast cancer. We present Semi-supervised Locality Discriminant Projections with Kernels for breast cancer classification. The contributions of this work lie in: 1) Semi-supervised learning is used into Locality Preserving Projections (LPP) to enhance its performance using side-information together with the unlabelled training samples, while current algorithms only consider the side-information but ignoring the unlabeled training samples. 2) Kernel trick is applied into Semi-supervised LPP to improve its ability in the nonlinear classification. 3) The framework of breast cancer classification with Semi-supervised LPP with kernels is presented. Many experiments are implemented on four breast tissue databases to testify and evaluate the feasibility and affectivity of the proposed scheme.
journal_name
J Med Systjournal_title
Journal of medical systemsauthors
Li JB,Yu Y,Yang ZM,Tang LLdoi
10.1007/s10916-011-9754-6subject
Has Abstractpub_date
2012-10-01 00:00:00pages
2779-86issue
5eissn
0148-5598issn
1573-689Xjournal_volume
36pub_type
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journal_title：Journal of medical systems
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journal_title：Journal of medical systems
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journal_title：Journal of medical systems
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journal_title：Journal of medical systems
pub_type： 已发布勘误
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