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CPM maximizes the class discrimination and also preserves approximately the class covariance. The optimization involved in CPM can be formulated as low rank ...
CPM max- imizes the class discrimination and also preserves ap- proximately the class covariance. The optimization involved in CPM can be formulated as low rank ...
In this paper, we propose a new algorithm for dimension reduction, called CPM (which stands for. Covariance-preserving Projection Method). CPM aims to ...
CPM maximizes the class discrimination and also preserves approximately the class covariance. The optimization involved in CPM can be formulated as low rank ...
The low-rank representation coefficients are used to acquire the constrained term is instigated by the adaptive graph. And by adding the un-correlation analysis ...
In this paper, a Pairwise Covariance-preserving Projection Method (PCPM) is proposed for dimension reduction. PCPM maximizes the class discrimination and also ...
Dimensionality reduction (DR) on the manifold includes effective methods which project the data from an implicit relational space onto a vectorial space.
Bibliographic details on CPM: A Covariance-preserving Projection Method.
We proposed a semi-supervised dimensionality reduction algorithm, called Semi- supervised Pairwise Covariance-preserving Projection Method (SPCPM), extending.
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A Pairwise Covariance-Preserving Projection Method for Dimension Reduction ; CPM: A Covariance-preserving Projection Method. Ye J., Xiong T., Janardan R. ; Q1.