Minimum Variance Embedded Random Vector Functional Link Network with Privileged Information

Abstract

A teacher in a school plays significant role in classroom while teaching the students. Similarly, learning via privileged information (LUPI) gives extra information generated by a teacher to 'teach' the learning algorithm while training. This paper proposes minimum variance embedded random vector functional link network with privileged information (MVRVFL+). The proposed MVRVFL+ minimizes the intraclass variance of the training data and uses privileged information paradigm which provides the additional knowledge during the training of the model. The proposed MVRVFL+ classification model is evaluated on 43 benchmark UCI datasets. From the experimental analysis, the proposed MVRVFL+ showed best average accuracy and emerged as the lowest average rank classifier among the baseline models.