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Recently, hash algorithms catch amounts of sights in the field of machine learning. Most existing hash methods directly utilize a vector, which can be piped by the column of image matrix, as a unit and adopt some feature extraction functions to project the original data into generally shorter fixed-length values or characters. Then each of these projected real values is quantized or hashed into zero-one...
In this paper, three new algorithms are presented by applying group idea and collaborative thought to projective dictionary pair learning (DPL). These algorithms further extend the framework of discriminative dictionary learning (DL). Based on projective dictionary pair learning which realizes the goals of signal representation and pattern classification by learning a synthesis dictionary and an analysis...
Spectral clustering (SC) is a fundamental technique in the field of data mining and information processing. The similarity matrix of the input data set plays a vital role in SC methods. Since the similarity matrix computation is independent of the clustering procedure, the final results may be suboptimal if the learned similarity is not optimal in SC methods. In this paper, we proposed a novel data...
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