Semi-supervised geometric mean of Kullback-Leibler divergences for subspace selection

Subspace selection is widely adopted in many areas of pattern recognition. A recent result, named maximizing the geometric mean of Kullback-Leibler (KL) divergences of class pairs (MGMD), is a successful method for subspace selection, which can significantly reduce the class separation problem. Howe...

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Bibliographische Detailangaben

Hauptverfasser:	Si-Bao Chen, Hai-Xian Wang, Xing-Yi Zhang, Bin Luo
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Covariance matrix Educational institutions Laplace equations Manifolds Optimization Symmetric matrices Training
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