Learning strictly orthogonal p-order nonnegative Laplacian embedding via smoothed iterative reweighted method
Laplacian embedding is a powerful graph based method with its ability in spectral clustering to reveal the intrinsic geometry of data in the high dimensional space. Imposing the orthogonality and the nonnegativity constraints can avoid degenerate and negative solutions, respectively. These two attributes are critical yet challenging to achieve simultaneously. Although, in recent years, many attempts have been made to overcome this, this problem is still not perfectly handled. We propose an effective algorithm to solve the Laplacian embedding problem that satisfies the both constraints. To promote ...
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