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tSNE vs MDS vs PCA

In this video, we take a closer look at Multidimensional scaling (MDS). We practice its use on a small data set. Then, using a data set that is much larger, we compare and contrast the clustering structures resulting from MDS, t-SNE and PCA. We conclude that on larger data sets all three methods produce vastly different results with t-SNE coming out on top as the most cluster-defining metod. In conclusion when we want to find clusters we use t-SNE, when we care about ALL the distances, we use MDS, and when we need some robust dimensionality reduction methods that use mathematical projection and retain as much variance as possible, we use PCA. This video is a part of Introduction to Data Science video series that dives into machine learning, visual analytics, and joys of interactive data analysis using Orange Data Mining software (https://orangedatamining.com). SUBSCRIBE to our channel:    / orangedatamining   The development of this video series was supported by grants from the Slovenian Research Agency (including P2-0209, V2-2274, and L2-3170), Slovenia Ministry of Digital Transformation, European Union (including xAIM and ARISA) and Google.org/Tides foundation. #machinelearning #orange #visualanalytics #datamining __ Written by: Blaž Zupan (http://biolab.si/blaz) Presented by: Noah Novšak Production and edit: Lara Zupan Intro/outro: Agnieszka Rovšnik Music by: Damjan Jović – Dravlje Rec Orange is developed by Biolab at University of Ljubljana (https://www.biolab.si)