The manifold regularized SVDD for noisy label detection

Abstract

Supervised Learning (SL) celebrates a lot of research topics in machine learning (ML) and provides a large number of applications in multimedia. The effectiveness and performance of supervised learning are highly dependent on the quality of labeled samples, and poor or mislabeled labels always damage pre-knowledge and produce disastrous results. One of the notable works is the development of a model for detecting noisy labels in large-scale data sets for supervised classification. This paper introduces a new method for noisy label detection that combines the support vector data description (SVDD) model with manifold regularization. The proposed method is able to detect the noisy label in the testing set. In addition, we adopt K nearest neighbors to obtain the K-nearest neighbor graph, and the k-core algorithm is used to select noise nodes by selecting the influential nodes with larger k values in the K-NN graph. Experimental results over UCI data sets show that the manifold regularized SVDD can effectively detect the noise labels data, and presents better performance over most methods in terms of the outlier detection accuracy, and the average accuracy is mostly above 50%, even 100%. Moreover, manifold regularized SVDD has good performance in detecting the most influential top-k nodes compared with other methods.