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Neighbourhood preserving based semi-supervised dimensionality reduction

Neighbourhood preserving based semi-supervised dimensionality reduction

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A semi-supervised linear dimensionality reduction method based on side information and neighbourhood preserving is proposed. In this problem, only must-link constraints (pairs of instances belong to the same class) and cannot-link constraints (pairs of instances belong to different classes) are given. The proposed neighbourhood preserving based semi-supervised dimensionality reduction algorithm can not only preserve the must-link and cannot-link constraints but can preserve the local structure of the input data in the low dimensional embedding subspace. Experimental results on several datasets demonstrate the effectiveness of the method.

Inspec keywords: learning (artificial intelligence)

Other keywords: semisupervised linear dimensionality reduction method; low dimensional embedding subspace; must-link constraints; cannot-link constraints

Subjects: Learning in AI (theory)

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