Quantization
2016-03-29 — 2023-11-02
Wherein the state-space of a system is discretized into representative regions, and its role in coding theory, compression, and mixture-model density approximation is delineated and linked to classification.
Bayes
classification
clustering
compsci
feature construction
information
networks
nonparametric
probability
sparser than thou
statistics
Quantisation of system state-space, considered in the abstract. Implicit in coding theory, compression, and mixture models.
Connection to classification.
A placeholder.
1 References
Changizi, and Shimojo. 2005. “Character Complexity and Redundancy in Writing Systems over Human History.” Proceedings of the Royal Society B: Biological Sciences.
Gerber, Pospisil, Navandar, et al. 2020. “Low-Cost Scalable Discretization, Prediction, and Feature Selection for Complex Systems.” Science Advances.
Horenko, Vecchi, Kardoš, et al. 2023. “On Cheap Entropy-Sparsified Regression Learning.” Proceedings of the National Academy of Sciences.
Peluffo-Ordónez, Lee, and Verleysen. 2014. “Short Review of Dimensionality Reduction Methods Based on Stochastic Neighbour Embedding.” In Advances in Self-Organizing Maps and Learning Vector Quantization.
Smola, Williamson, Mika, et al. 1999. “Regularized Principal Manifolds.” In Computational Learning Theory. Lecture Notes in Computer Science 1572.
Vecchi, Pospíšil, Albrecht, et al. 2022. “eSPA+: Scalable Entropy-Optimal Machine Learning Classification for Small Data Problems.” Neural Computation.