Multi-user authentication from keystroke data is an open problem that needs to be solved. When an online account or a common desktop is shared among multiple users, there is a need to determine who the current user is at log-in. In this paper, a non-linear feature transformation-based multi-user classification algorithm is proposed. Quantile transformation is proposed to map raw keystroke features to a uniform distribution to limit outliers. Then, dimensionality reduction techniques such as PCA, Kernel-PCA, and t-SNE are applied to the transformed features to project into reduced feature space. Unsupervised clustering algorithms such as DBSCAN and GMM are applied in the reduced feature space to identify the number of users accessing the system. Using these results, a k-nearest neighbor search algorithm is used, in conjunctions with labeled clusters to classify users. The algorithm is validated using the CMU keystroke benchmark dataset and the MobiKey touch dataset. Once we identify the number of users, we can successfully classify users with an accuracy of over 93 percent.