Cluster-Based Quantization Methods

Updated 22 November 2025

Cluster-based quantization methods are techniques that replace continuous values with representative centroids derived from clustering to minimize distortion.
They employ classical and modern k-means, weighted clustering, and differentiable assignments to refine quantization and improve performance.
These methods are widely applied in neural network compression, image coding, and quantum integrable systems to balance efficiency and accuracy.

Cluster-based quantization methods formalize quantization as a clustering problem, leveraging the statistical structure of data—weights, activations, or features—by replacing real-valued variables with representative centroids determined by cluster analysis. These methods are central in diverse areas such as neural network model compression, efficient image representation, quantum integrable systems, and cluster algebra quantization. Cluster-based quantization spans a range of methodologies, from classical $k$ -means quantization of vectors, to block-structured or weighted clustering for modern deep models, and to the quantization of cluster varieties in the context of mathematical physics.

1. Mathematical Foundations of Cluster-Based Quantization

Cluster-based quantization typically involves partitioning an input set (e.g., real numbers, feature vectors, or tensor blocks) into $k$ clusters and representing each data point by the centroid of its assigned cluster. The canonical objective is minimization of distortion, expressed as:

$\min_{\{C_j\}_{j=1}^k,\; \{\mu_j\}_{j=1}^k} \sum_{j=1}^k \sum_{x_i \in C_j} \|x_i - \mu_j\|_2^2$

The centroid update for each cluster $j$ with assignment $C_j$ is:

$\mu_j = \frac{1}{|C_j|} \sum_{x_i \in C_j} x_i$

Algorithms such as Lloyd's $k$ -means perform iterative assignment and centroid update steps until convergence. Extensions include weighting (e.g., for pixel frequencies in color quantization (Celebi, 2010)), block treatment (for tensors (Elangovan et al., 7 Feb 2025)), and generalized distortion metrics (e.g., $L^r$ loss in stochastic quantization (Kozyriev et al., 2024)).

In quantum cluster varieties and integrable systems, cluster variables and their mutations form the structural backbone. Fock–Goncharov quantization promotes cluster variables to non-commuting operators, introducing quantum parameters and operator-valued mutations obeying prescribed commutation relations (Kim, 2016, Cheung et al., 2020).

2. Algorithms and Variations in Modern Cluster-Based Quantization

a. Scalar and Vector Clustering for Compression

Classical $k$ -means is foundational for scalar quantization, and appears in both post-processing and integrated compression pipelines (e.g., color quantization, image codecs) (Hoeltgen et al., 2017, Celebi, 2010).
Block-based clustering (BCQ/LO-BCQ) partitions large tensors into blocks, clusters these blocks, and designs per-cluster quantizers (codebooks), yielding superior accuracy in low-bit regimes for LLMs and deep models (Elangovan et al., 7 Feb 2025). Each codebook serves as the cluster centroid for quantization purposes.
Weighted $k$ -means adapts the objective with sample-dependent importance weights, for instance, to favor fidelity in blocks with high activation (Xu et al., 2 May 2025).
Sparse least-squares quantization recasts clustering via assignment matrices and centroids, allowing for equivalence with improved $k$ -means and directly minimizing quantization error (Wang et al., 2018).

b. Data-free and Feature-Aligned Cluster-Based Quantization

ClusterQ aligns the distributions of feature clusters arising in deep feature space. Deep features are partitioned into clusters by semantic class; synthetic data generation aims to reproduce per-class cluster statistics from the original model, thereby maintaining inter-class separability under heavy quantization (Gao et al., 2022).

c. Differentiable Cluster-Based Quantization

Implicit/Differentiable $k$ -means (IDKM) introduces attention-based soft assignment and implicit gradients for quantizer optimization, yielding low-memory, differentiable quantization with state-of-the-art compression-accuracy trade-offs (Jaffe et al., 2023).
Cluster-Promoting Quantization (CPQ) leverages probabilistic soft quantization, a multi-class straight-through estimator, and bit-drop regularization (DropBits) to enforce clustering in parameter space while enabling learnable, heterogeneous bit assignments (Lee et al., 2021).

d. Post-Training and Output-Aware Cluster-Based Correction

CAT (Cluster-based Affine Transformation) applies cluster-specific affine mappings at the logit level to correct systematic quantization error. A PCA and $k$ -means procedure in the logit space produces clusters, for which closed-form mean/variance matching yields optimal affine parameters, enhancing PTQ accuracy (Zoljodi et al., 30 Sep 2025).

e. High-Dimensional and Adaptive Clustering via Stochastic Optimization

Stochastic Quantization (SQ) applies an online, SGD-like update to cluster centers, minimizing global distortion with provable convergence under standard step-size conditions (Kozyriev et al., 2024). Such approaches operate efficiently in streaming and high-dimensional regimes, particularly when paired with dimension reduction (e.g., triplet-network embeddings).

3. Applications across Domains

a. Deep Neural Network Quantization

Cluster-based quantization methods have become crucial for low-bit model deployment, post-training quantization, and mixed-precision schemes.

Weights: Partitioning weights into clusters reduces precision with minimal accuracy loss. Adaptive schemes (e.g., CPQ, IDKM, RWKVQuant, BCQ) enable per-layer, per-block, or per-vector clustering tailored to data geometry (Lee et al., 2021, Jaffe et al., 2023, Xu et al., 2 May 2025, Elangovan et al., 7 Feb 2025).
Activations: Channel-wise or block-wise clustering enables fine-grained quantization of activations, especially for large models with significant variation in activation dynamic range (Yuan et al., 2023).
Hybrid schemes: Integration of scalar and vector clustering is shown to optimally balance codebook overhead, memory, and accuracy in models with heterogeneous parameter distributions (Xu et al., 2 May 2025).

b. Image and Signal Compression

Clustering-based quantization underpins a broad array of image coding schemes, from color quantization and palette design (Celebi, 2010), to PDE-based inpainting codecs where clustering is performed over pixel values, spatial coordinates, or histogram bins (Hoeltgen et al., 2017).
Empirically, clustering over raw or histogram-weighted values offers the best trade-off between reconstruction error and storage, particularly when embedded in larger rate-distortion optimization loops.

c. Data-Free Calibration

ClusterQ advances data-free quantization settings by clustering internal features (e.g., BatchNorm statistics) and using feature-aligned synthetic generation, closing the gap to data-aware approaches in the low-bit regime (Gao et al., 2022).

d. Mathematical Physics, Cluster Algebras, and Quantum Integrable Systems

In Fock–Goncharov quantization and related cluster algebra frameworks, cluster mutations define the algebraic structure on varieties, and their quantization yields non-commutative deformations of function algebras, quantum mutation automorphisms, and constructions of quantum integrable systems (Kim, 2016, Cheung et al., 2020, Franco et al., 2015).
These cluster-based quantizations underlie the quantization of geometric $R$ -matrices, $q$ -Painlevé equations, quantum Teichmüller theory, and more (Inoue et al., 2016, 1711.02063).

4. Implementation Workflow and Practical Considerations

Cluster-based quantization methods demand careful initialization, clustering strategy, and assignment for practical efficacy.

Initialization: $k$ -means++ or density-aware initialization alleviates poor local minima and empty clusters, especially in high-resolution or weighted settings (Celebi, 2010, Wang et al., 2018, Xu et al., 2 May 2025).
Assignment and Update: Iterative assignment of data to centroids and update of centroids is standard; for memory or real-time constraints, stochastic or online updates can be employed (SQ, mini-batch $k$ -means) (Kozyriev et al., 2024).
Storage overhead: Methods like BCQ explicitly account for codebook storage, block assignment index cost, and per-array scaling in estimating effective bitwidth per scalar (Elangovan et al., 7 Feb 2025).
Integration with entropy coding: The match between clustering results and entropy-coded representations is crucial; non-uniform indices often lead to higher entropy and index cost, offsetting gains in mean squared error (Hoeltgen et al., 2017).
Adaptivity: Proxy-guided selection of quantization type (scalar vs. vector) accommodates layers with uniform vs. multimodal weight distributions (Xu et al., 2 May 2025).

5. Empirical Performance and Limitations

Extensive experiments across vision, language, and physical systems show that cluster-based quantization achieves:

Method/Domain	Main Empirical Results	Key References
Neural network PTQ	W4A4 quantization with <1% accuracy loss on Llama2-70B, GPT3-22B, etc.; 2–3× memory savings	(Elangovan et al., 7 Feb 2025)
Data-free quantization	ClusterQ beats prior DFQ schemes (e.g., ZeroQ, Qimera), achieving +0.6–2% in Top-1 accuracy	(Gao et al., 2022)
Output-aware PTQ	CAT lifts W2A2 ResNet-18 Top-1 accuracy by +0.4–1.25% with negligible overhead	(Zoljodi et al., 30 Sep 2025)
RWKV quantization	<1% drop at 3 bits/weight; codebook optimization yields up to 4-point perplexity gain over naive VQ	(Xu et al., 2 May 2025)
PDE-based codecs	Clustering-based quantization achieves lower MSE but sometimes higher compressed file size vs. uniform quant	(Hoeltgen et al., 2017)

However, in highly uniform layers or when index cost dominates, uniform quantization may still yield higher compression ratios (Hoeltgen et al., 2017, Xu et al., 2 May 2025). The presence of non-uniform cluster sizes or entropy inefficiencies remains a challenge.

6. Cluster-Based Quantization in Quantum Algebra and Integrable Systems

Cluster algebras furnish an algebraic and combinatorial foundation for the quantization of a broad class of integrable systems and moduli spaces.

Fock–Goncharov Quantum Tori: Quantum cluster variables obey non-commutative multiplication, and cluster mutations induce automorphisms via quantum dilogarithm conjugation (Kim, 2016). In deformed cluster Poisson varieties, families of quantum $\mathcal{X}$ -mutation isomorphisms glue coefficient-parameterized quantum tori, extending the original construction and ensuring each fiber retains a Poisson structure (Cheung et al., 2020).
Cluster Integrable Systems: Quantization of GK cluster systems involves Weyl quantization of spectral curves, with exact quantization conditions specified by enumerative-geometry data (e.g., refined BPS invariants) and the quantum mirror map. These methods yield spectral data matching with direct diagonalization of quantum Hamiltonians (Franco et al., 2015).
Quantum $R$ -matrices and $q$ -Painlevé Equations: Cluster mutations on quivers generate geometric $R$ -matrix actions, admissible for quantization by lifting variables to non-commutative generators. The resulting quantum geometric $R$ -matrix and quantum loop symmetric functions form complete sets of invariants and satisfy braid/Yang–Baxter or Painlevé-type identities (Inoue et al., 2016, 1711.02063).

7. Theoretical Insights, Extensions, and Limitations

Cluster-based quantization possesses strong theoretical underpinnings: convergence guarantees (as in stochastic quantization), operator-theoretic constructions for cluster varieties, and exact moment matching in cluster-aware affine correction. Notable caveats and recent findings include:

Quantum Laurent Positivity Breakdown: Even when quantum cluster mutation maps remain Laurent-positive, the global theta basis (in rank 2 and beyond) can admit negative coefficients, countering classical positivity conjectures (Cheung et al., 2020).
Expressive Limitations: In some data regimes, e.g., nearly uniform distributions or overwhelmingly redundant features, clustering confers marginal gains or can be subsumed by entropy-aware uniform quantization (Xu et al., 2 May 2025, Hoeltgen et al., 2017).
Practical Integration: Efficiency, codebook storage, and compatibility with modern hardware (e.g., block-based fetch/gather, integer-matrix mul support) are central concerns in deploying cluster-based quantization at scale (Elangovan et al., 7 Feb 2025).

Cluster-based quantization bridges mathematical theory, algorithmic innovation, and practical deployment. Its applicability ranges from deployable ultra-low-bit quantization and robust, data-free calibration, to the rigorous quantization of moduli spaces and quantum integrable systems, with each instantiation leveraging clustering as the central device for minimizing representation error, enabling new structures in both applied and theoretical settings (Inoue et al., 2016, Elangovan et al., 7 Feb 2025, Gao et al., 2022, Franco et al., 2015, Jaffe et al., 2023, Zoljodi et al., 30 Sep 2025, Lee et al., 2021, Hoeltgen et al., 2017, Celebi, 2010, Xu et al., 2 May 2025, Cheung et al., 2020).