Integrating the Supervised Information into Unsupervised Learning

Abstract EN

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 twophases: extracting and clustering data representatives (DRs) firstly to obtain labeled training data and then classifyingnon-DRs based on labeled DRs.

The implementation algorithm is called SDSN since it employs the tuning-scaledSupport vector domain description to collect DRs, uses spectrum-based method to cluster DRs, and adopts thenearest neighbor classifier to label non-DRs.

The validation of the clustering procedure of the first-phase is analyzedtheoretically.

A new metric is defined data dependently in the second phase to allow the nearest neighbor classifier towork 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 andSDSN exhibits higher popularity in practice over the traditional pure clustering procedure.