Randomized nonlinear component analysis

Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved. Classical methods such as Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are ubiquitous in statistics. However, these techniques are only able to reveal linear re-lationships in data. Although nonlinear variants of PCA and CCA have been proposed, these are computationally prohibitive in the large scale. In a separate strand of recent research, randomized methods have been proposed to construct features that help reveal nonlinear patterns in data. For basic tasks such as regression or classification, random features exhibit little or no loss in performance, while achieving drastic savings in computational requirements. In this paper we leverage randomness to design scalable new variants of nonlinear PCA and CCA; our ideas extend to key multivariate analysis tools such as spectral clustering or LDA. We demonstrate our algorithms through experiments on real- world data, on which we compare against the state-of-the-art. A simple R implementation of the presented algorithms is provided.

Defining product-service network configurations and location roles: A current and future state analysis framework for international engineering operations

Many manufacturing firms have developed a service dimension to their product portfolio. In response to this growing trend of servitisation, organisations, often involved in complex, long-lifecycle product-service system (PSS) provision, need to reconfigure their global engineering networks to support integrated PSS offerings. Drawing on parallel concepts in 'production' networks, the idea of 'location role' now becomes increasingly complex, in terms of service delivery. As new markets develop, locations in a specific region may need to grow/adapt engineering service 'competencies' along the value chain, from design and build to support and service, in order to serve future location-specific requirements and, potentially, those requirements of the overall network. The purpose of this paper is to advance understanding of how best to design complex multi-organisational engineering service networks, through extension of the 'production' network location role concept to a PSS context, capturing both traditional engineering 'design and build' and engineering 'service' requirements. Copyright © 2012 Inderscience Enterprises Ltd.