Integrating the Supervised Information into Unsupervised Learning

Abstract

This paper presents an assembling unsupervised learning framework that adopts the information coming from the supervised learning process and gives the corresponding implementation algorithm. The algorithm consists of two phases: extracting and clustering data representatives (DRs) firstly to obtain labeled training data and then classifying non-DRs based on labeled DRs. The implementation algorithm is called SDSN since it employs the tuning-scaled Support vector domain description to collect DRs, uses spectrum-based method to cluster DRs, and adopts the nearest neighbor classifier to label non-DRs. The validation of the clustering procedure of the first-phase is analyzed theoretically. A new metric is defined data dependently in the second phase to allow the nearest neighbor classifier to work with the informed information. A fast training approach for DRs’ extraction is provided to bring more efficiency. Experimental results on synthetic and real datasets verify that the proposed idea is of correctness and performance and SDSN exhibits higher popularity in practice over the traditional pure clustering procedure.