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

A new fast instance selection method for machine learning.

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

The code for prototype selection and instance ranking using matrix decomposition and subspace learning

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.

The code for Principal Sample Analysis (PSA) for prototype selection and instance ranking

The code for Curvature Anomaly Detection (CAD), kernel CAD, inverse CAD (iCAD), and kernel iCAD for anomaly detection and prototype selection. It uses the theory of Polyhedron Curvature or angular defect of polyhedron.

Fast instance selection method

Border Instances Reduction using Classes Handily (BIRCH)

Unsupervised instance selection via conjectural hyperrectangles