Cognitive gravitation model for classification on small noisy data

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 is applied. Subsequently, a new classifier is designed which utilizes CGM to find k nearest neighbors from each class for the query sample and then classifies this query sample to the class whose cognitive gravitation is largest. The cognitive gravitation of the class is defined as the sum of the cognitive gravitation between its each nearest neighbor and the query sample. The advantage of our approach is that it has a firm and simple mathematical basis while it has good classification performance. The conducted experiments on challenging benchmark data sets validate the proposed model and the classification approach.