t-SNE and data visualization
t-SNE is a technique for visualizing high-dimensional data by mapping it into a lower-dimensional space, typically two or three dimensions. This method is particularly effective for revealing the underlying structure of complex datasets, making it easier to interpret and analyze patterns, clusters, and relationships within the data.
Getting ready
For our t-SNE demonstration, we’ll be using a different dataset: another famous machine learning dataset called MNIST, which consists of images of handwritten digits 0-9. This is from the UCI Machine Learning Repository: We used preprocessing programs made available by NIST to extract normalized bitmaps of handwritten digits from a preprinted form. From a total of 43 people, 30 contributed to the training set and different 13 to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of 4x4 and the number [sic] of on pixels are counted in each block. This generates an input matrix of 8x8...
