# Advice on applying Machine learning for high dimentional datasets

I am working with a data-set of around ~100000 observations(rows) and ~256 features(columns). Is there any recommendation for applying Machine Learning techniques on such a data-set efficiently ? Maybe by parallelization or similar approaches ?

I am currently using Matlab for applying different Machine Learning, but have investigated Python's scikit-learn as well for applying:

Regression

• Gaussian processes for regression GPR

Classification

• Linear discriminant analysis LDA
• Support vector machine SVM

Obviously dimentionality reduction comes to mind, however for this specific data-set removing some of the features or applying transformations will distort the information.

• That's less than 200 MB of data in MATLAB double precision. That doesn't sound very big to me. – Mark L. Stone Dec 10 '17 at 18:19
• @MarkL.Stone The PC is crashing every time i run GPR on the full dataset, or it just runs for hours without any results at the end and i would have to stop it manualy – AnarKi Dec 10 '17 at 18:21
• Lots of options and methods (algorithms) available mathworks.com/help/stats/fitrgp.html . Some might make a big difference in run time and success prospects. Have you set verbose to 2 so that you can see intermediate output, progress (or not) being made? – Mark L. Stone Dec 10 '17 at 18:30
• Your PC is crashing because out of the box gaussian process regression is $\mathcal{O}(n^3)$ (it requires the inversion of the gram matrix). If you want to scale gaussian processes to more than a few thousand data points you need to look into approximate inference methods that specifically can handle 100k data points. – aleshing Dec 10 '17 at 18:52
• You problem doesn’t qualify as a big data problem. I suggest revising the title of your question. – aivanov Dec 10 '17 at 19:00