Unsupervised Learning

kmeans

t-SNE

t-Distributed Stochastic Neighbor Embedding (t-SNE) is a technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets. The technique can be implemented via Barnes-Hut approximations, allowing it to be applied on large real-world datasets. We applied it on data sets with up to 30 million examples.‌

​https://github.com/ztlevi/t-sne-python/blob/master/tsne.py

Principal Component Analysis (PCA)

Statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components.

• Principal Component Analysis (PCA) is a dimension reduction technique that projects the data into a lower dimensional space

• PCA uses Singular Value Decomposition (SVD), which is a matrix factorization method that decomposes a matrix into three smaller matrices (more details of SVD here)

• PCA finds top N principal components, which are dimensions along which the data vary (spread out) the most. Intuitively, the more spread out the data along a specific dimension, the more information is contained, thus the more important this dimension is for the pattern recognition of the dataset

• PCA can be used as pre-step for data visualization: reducing high dimensional data into 2D or 3D. An alternative dimensionality reduction technique is t-SNE

Here is a visual explanation of PCA