A New Method for Steganalysis Techniques:IKLDA
Guoming Chen · Dianzi xuebao · 2009
Reducing the dimensionality of data without losing intrinsic information is a hotspot in machine learning and data mining.In this paper we propose a new dimensionality reduction algorithms call IKLDA(improved kernel Linear discriminant analysis) on the ground of graph embedding framework.Our method not only can detect the information hidden in digital images but also reduce the dimensionality.Theoretical analysis and experiments show that our new IKLDA algorithm is effective in steganalysis and is more precise than the other traditional dimensional reduction methods.Furthermore,our method promotes development of visualization in the application in steganalysis.