A Winner-Take-All Hashing-Based Unsupervised Model for Entity Resolution Problems. [B. Sc. Thesis]

A library gathering diverse algorithms for clustering, similarity search, prototype selection, and data encoding based on k-cluster algorithms.

This is a course project to select a subset of data to build an efficient nearest neighbor classifier. Choosing a representative subset of "prototypes" from the training set is crucial for accelerating nearest neighbor classifiers. This project proposes projecting the data into a latent space using a pretrained embedder.

Fast instance selection method