Learning to Learn

Hi, In this blog I will be discussing some trade-offs we make while choosing a dimensionality reduction technique for our problem. Now, let's jump into this directly. Dimensionality reduction(DR) reduces higher dimensional data to lower dimensions. Or we can say that DR maps -dimensional data into -dimensions ( ), ( ), where these new -dimensions hold nearly all of the relevant information about the original data. Sometimes DR results can show clusters of data that are not even present in the original data and sometimes it can map two neighbors from the higher dimension far into the lower dimension. So let's discuss and compare some methods which can prevent these problems. I will be discussing t-SNE, UMAP, and TriMap in this blog.  1. t-SNE (t-distributed Stochastic Neighborhood Embedding) t-SNE uses the distance between two points in higher dimensions and maps it to the lower dimension.   where   is computed by using binary search in the equation, Perplexity = ...