A Self-Organizing Clustering System for Unsupervised Distribution Shift Detection

arXiv:2404.16656v1 Announce Type: new
Abstract: Modeling non-stationary data is a challenging problem in the field of continual learning, and data distribution shifts may result in negative consequences on the performance of a machine learning model. Classic learning tools are often vulnerable to perturbations of the input covariates, and are sensitive to outliers and noise, and some tools are based on rigid algebraic assumptions. Distribution shifts are frequently occurring due to changes in raw materials for production, seasonality, a different user …

abstract arxiv clustering consequences continual cs.ai cs.lg cs.ne data detection distribution learning tools machine machine learning machine learning model modeling negative performance shift tools type unsupervised vulnerable