Робастна кластеризація великих даних методом стохастичного квантування

• Date: 2025
• Main Authors: Kozyriev, Anton, Norkin, Vladimir
• Format: Article
• Language: English
• Published: V.M. Glushkov Institute of Cybernetics of NAS of Ukraine 2025
• Subjects: стохастичне квантування; алгоритм кластеризації; алгоритм K-середніх; стохастичний градієнтний спуск; неопукла оптимізація
• Online Access: https://jais.net.ua/index.php/files/article/view/438
• Journal Title: Problems of Control and Informatics

This paper addresses the limitations of traditional vector quantization (clustering) algorithms, particularly K-means and its variant K-means++, and explores the stochastic quantization (SQ) algorithm as a scalable alternative for high-dimensional unsupervised and semi-supervised learning problems. Some traditional clustering algorithms suffer from inefficient memory utilization during computation, necessitating the loading of all data samples into memory, which becomes impractical for large-scale datasets. While variants such as mini-batch K-means partially mitigate this issue by reducing memory usage, they lack robust theoretical convergence guarantees due to the non-convex nature of clustering problems. In contrast, SQ-algorithm provides strong theoretical convergence guarantees, making it a robust alternative for clustering tasks. We demonstrate the computational efficiency and rapid convergence of the algorithm on an image classification problem with partially labeled data. To address the challenge of high dimensionality, we trained Triplet Network to encode images into low-dimensional representations in a latent space, which serve as a basis for comparing the efficiency of both SQ-algorithm and traditional quantization algorithm.