UNSUPERVISED-ML

Principal Component Analysis | Classical PCA | Kernel PCA

Principal component analysis (PCA) is a powerful tool for data processing and dimensionality reduction. Its objective is to principally determine a subspace that explains most of the variance of the data, by projecting data into this subspace of k less than the original space N.

KMeans Clustering | Classical KMeans | kernel Kmeans

K-Means clustering is a fast, robust, and simple algorithm that gives reliable results when data sets are distinct or well separated from each other in a linear fashion. It is best used when the number of cluster centers, is specified due to a well-defined list of types shown in the data.

Gaussian Mixture Model | Classical GMM | kernel GMM

Gaussian Mixture Component is a probabilistic soft clustering algorithm which assumes that data points can be generated by any distribution in a mixture of observed Gaussians with some reasonable probabilities.