mlsanityData to 13 Oct 2026

An efficient and straightforward online quantization method for a data stream through remove-birth updating

arXiv 2306.12574 ↗First posted 21 Jun 2023cs.LG cs.DBcited by 0

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Abstract (arXiv)

The growth of network-connected devices has led to an exponential increase in data generation, creating significant challenges for efficient data analysis. This data is generated continuously, creating a dynamic flow known as a data stream. The characteristics of a data stream may change dynamically, and this change is known as concept drift. Consequently, a method for handling data streams must efficiently reduce their volume while dynamically adapting to these changing characteristics. This paper proposes a simple online vector quantization method for concept drift. The proposed method identifies and replaces units with low win probability through remove-birth updating, thus achieving a rapid adaptation to concept drift. Furthermore, the results of this study show that the proposed method can generate minimal dead units even in the presence of concept drift. This study also suggests that some metrics calculated from the proposed method will be helpful for drift detection.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 3 of its 47 references that are in the map. First posted on arXiv 21 Jun 2023.

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3 of 47 references.

References · 3

  1. 2020Data Stream Clustering: A Reviewcited by 14
  2. 2020Approximate spectral clustering using both reference vectors and topology of the network generated by growing neural gascited by 2
  3. 2022Sampling Streaming Data with Parallel Vector Quantization -- PVQcited by 1

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  1. 2022Unsupervised Unlearning of Concept Drift with Autoencoders
  2. 2025Enhancing Vector Quantization with Distributional Matching: A Theoretical and Empirical Study
  3. 2024Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE
  4. 2026IVF-TQ: Calibration-Free Streaming Vector Search via a Codebook-Free Residual Layer
  5. 2026Beyond Stationarity: Rethinking Codebook Collapse in Vector Quantization
  6. 2024Locally-Adaptive Quantization for Streaming Vector Search
  7. 2026Unlearning-based sliding window for continual learning under concept drift
  8. 2026VQ-bench: A Composable Vector Quantization Framework