A Survey on Unsupervised Machine Learning Algorithms for Automation, Classification and Maintenance

Abstract

The paper is comprehensive survey of methodologies and techniques used for Unsupervised Machine Learning that are used for learn complex, highly non-linear models with millions parameters to used large amount of unlabeled data. Deep belief networks (DBNs) and sparse coding are the two well known techniques of unsupervised learning models. Data clustering distinguishes by the absence of category information. Basically structure in data is finding in clustering and it has long history in scientific field .K-means is the most popular and simple clustering algorithm. This Algorithm was published in 1955. Hierarchical matching pursuit (HMP) for RGB-D data is discussed. Sparse coding learns hierarchical feature representations from raw RGB-D data in an unsupervised way by using hierarchical matching pursuit. The formal study of learning systems is deduced from Machine learning; which is a field of research. It has found to be highly interdisciplinary field which acquires and constructs upon ideas from statistics, computer science (engineering), optimization theory, and numerous other disciplines of science and mathematics.