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Recently, the low-rank representation (LRR) has been widely used in computer vision and pattern recognition with great success owing to its effectiveness and robustness for data clustering. However, the traditional LRR mainly focuses on the data from Euclidean space and is not directly applicable to manifold-valued data. A way to extend the LRR model from Euclidean space to the Stiefel manifold, by...
Twin kernel embedding (TKE) is a powerful non-vectorial data reduction algorithm proposed for advanced applications in clustering and visualization, manifold learning, etc. Due to the requirement of online processing in many cutting edge research problems involving highly structured data like DNA, protein sequences and biometric features that are non-vectorial in nature, learning the out-of-sample...
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