Semi-supervised clustering using seeded-kMeans in the feature space of ELM

Abstract

Extreme learning machine (ELM) is based on single layer feed forward neural networks (SLFNs) and has become a rapidly developing learning technology today. Recently developed Multilayer form of ELM called ML-ELM which is based on the architecture of deep learning, become more popular compared to other traditional classifiers because of its important qualities such as multiple non-linear transformation of input data, higher level abstraction of data, learning different form of input data, capable of managing huge volume of data etc. In addition to the above, another good quality which ML-ELM possesses is its ability to map the input feature vector non-linearly to an extended dimensional feature space for giving better performance. This paper proposes an approach where unsupervised and semi-supervised clustering using kMeans and seeded-kMeans have been done in ML-ELM feature space. The empirical results of the proposed approach on two benchmark datasets outperform the results of clustering done in TF-IDF vector space. Also, it is observed that in ML-ELM feature space, the results of seeded-kMeans are better compared to the traditional kMeans.