Optimal embedding for watermarking in discrete data spaces

In this paper, we address the problem of robust information embedding in digital data. Such a process is carried out by introducing modifications to the original data that one would like to keep minimal. It assumes that the data, which includes the embedded information, is corrupted before the extraction is carried out. We propose a principled way to tailor an efficient embedding process for given data and noise statistics. © Springer-Verlag Berlin Heidelberg 2005.

Outlier-resisting graph embedding

Graph embedding is a general framework for subspace learning. However, because of the well-known outlier-sensitiveness disadvantage of the L2-norm, conventional graph embedding is not robust to outliers which occur in many practical applications. In this paper, an improved graph embedding algorithm (termed LPP-L1) is proposed by replacing L2-norm with L1-norm. In addition to its robustness property, LPP-L1 avoids small sample size problem. Experimental results on both synthetic and real-world data demonstrate these advantages. © 2009 Elsevier B.V. All rights reserved.