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In many modern data sets, High dimension low sample size (HDLSS) data isprevalent in many fields of studies. There has been an increased focus recentlyon using machine learning and statistical methods to mine valuable informationout of these data sets. Thus, there has been an increased interest in efficientlearning in high dimensions. Naturally, as the dimension of the input dataincreases, the learning task will become more difficult, due to increasingcomputational and statistical complexities. This makes it crucial to overcomethe curse of dimensionality in a given dataset, within a reasonable time frame,in a bid to obtain the insights required to keep a competitive edge. To solveHDLSS problems, classical methods such as support vector machines can beutilised to alleviate data piling at the margin. However, when we questiongeometric domains and their assumptions on input data, we are naturally lead toconvex optimisation problems and this gives rise to the development ofsolutions like distance weighted discrimination (DWD), which can be modelled asa second-order cone programming problem and solved by interior-point methodswhen sample size and feature dimensions of the data is moderate. In this paper,our focus is on designing an even more scalable and robust algorithm forsolving large-scale generalized DWD problems.

A Foray into Parallel Optimisation Algorithms for High Dimension Low Sample Space Generalized Distance Weighted Discrimination problems