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The classifiers based on the relative transformation have good effectiveness in classification on the noisy, sparse and high-dimensional data. However, the relative transformation only simply transforms features from the original space to the relative space by Euclidean distances. It still ignores many other human perceptions. For example, to identify an object, human may find the difference among...
When performing the classification on the high dimensional, the sparse, or the noisy data, many approaches easily lead to the dramatic performance degradation. To deal with this issue from the different perspective, this paper proposes a cognitive gravitation model (CGM) based on both the law of gravitation in physics and the cognitive laws, where the self-information of each sample instead of mass...
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