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Recently, Blind Source Separate (BSS) technique has been extended to digital watermarking field. Slowly Feature Analysis (SFA)-a kind of BSS technique-is a new unsupervised learning algorithm to learn nonlinear functions that extract slowly varying signals out of the input data. It expediently can be used to extract image feature and separate the mixed signals. Making use of the advantages of SFA,...
Obtaining invariant representation of time varying signals is one of the major problems in object recognition. Recently, a new method that slowly feature analysis (SFA) which can extract invariant features of temporally varying signals is being explored, which is an extension of independent component analysis (ICA) which has been used for extracting facial feature. The technique of SFA can be extended...
Identifying database corresponding attributes in schema matching plays a key role in data integration in heterogeneous databases. Most of current approaches mainly use schema information of attribute. Little research has attempted to fully explore the use of data content. This paper introduces a novel schema matching algorithm based on data content, which has two-step process. First, through the analysis...
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