On the public citation graph

QuicK-means: accelerating inference for K-means by learning fast transforms

https://doi.org/10.1007/s10994-021-05965-0

0 signed · 32 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-70139-4openalex-snapshot

Neural Information Processing

inferredcites →https://doi.org/10.1007/s10107-013-0701-9openalex-snapshot

Proximal alternating linearized minimization for nonconvex and nonsmooth problems

inferredcites →https://doi.org/10.1016/j.patrec.2009.09.011openalex-snapshot

Data clustering: 50 years beyond K-means

inferredcites →https://doi.org/10.1109/cvpr42600.2020.01204openalex-snapshot

Butterfly Transform: An Efficient FFT Based Neural Architecture Design

inferredcites →https://doi.org/10.1109/icassp.2017.7953382openalex-snapshot

Compressive K-means

inferredcites →https://doi.org/10.1109/jstsp.2016.2543461openalex-snapshot

Flexible Multilayer Sparse Approximations of Matrices and Applications

inferredcites →https://doi.org/10.1109/tit.2014.2375327openalex-snapshot

Randomized Dimensionality Reduction for $k$ -Means Clustering

inferredcites →https://doi.org/10.1109/tpami.2014.2321376openalex-snapshot

Scalable Nearest Neighbor Algorithms for High Dimensional Data

inferredcites →https://doi.org/10.1111/(issn)1467-9876openalex-snapshot

Journal of the Royal Statistical Society: Series C (Applied Statistics)

inferredcites →https://doi.org/10.1137/1.9781611972801.12openalex-snapshot

Making k -means even faster

inferredcites →https://doi.org/10.1137/15m1007173openalex-snapshot

Butterfly Factorization

inferredcites →https://doi.org/10.1145/1772690.1772862openalex-snapshot

Web-scale k-means clustering

inferredcites →https://doi.org/10.1145/321879.321881openalex-snapshot

The Linear Complexity of Computation

inferredcites →https://doi.org/10.1609/aaai.v31i1.10852openalex-snapshot

Compressed K-Means for Large-Scale Clustering

inferredcites →https://doi.org/10.2307/2346830openalex-snapshot

Algorithm AS 136: A K-Means Clustering Algorithm

inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot

Scikit-learn: Machine Learning in Python

inferredcites →https://doi.org/10.48550/arxiv.1601.05775openalex-snapshot

Local Network Community Detection with Continuous Optimization of Conductance and Weighted Kernel K-Means

inferredcites →https://doi.org/10.48550/arxiv.1708.07747openalex-snapshot

Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

inferredcites →https://doi.org/10.48550/arxiv.1903.05895openalex-snapshot

Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations

inferredcites →https://doi.org/10.48550/arxiv.2007.08864openalex-snapshot

Sparse Linear Networks with a Fixed Butterfly Structure: Theory and Practice

inferredcites →https://doi.org/10.5555/1283383.1283494openalex-snapshot

k-means++: the advantages of careful seeding

inferredcites →https://doi.org/10.5555/1756006.1953024openalex-snapshot

Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4835463openalex-extracted

Dimensionality Reduction with Adaptive K-Nearest Neighbors Joint Fuzzy C-Means

inferred← citeshttps://doi.org/10.1007/978-981-95-8269-3_51openalex-snapshot

Predictive Modelling and Application of Total Organic Carbon Content in Geophysical Logging of Deep and Thin Clastic Formations in the Southern Ordos Basin

inferred← citeshttps://doi.org/10.1016/j.asoc.2025.113627openalex-snapshot

3D CAD model dynamic clustering based on inertial feature encoder

inferred← citeshttps://doi.org/10.1016/j.eswa.2023.122748openalex-snapshot

Efficient fuzzy-pruned high dimensional clustering with minimal distance measure

inferred← citeshttps://doi.org/10.1016/j.ress.2024.110457openalex-snapshot

An efficient reliability evaluation method for large-scale multi-performance multi-state series-parallel systems considering multi-dimensional approximation

inferred← citeshttps://doi.org/10.1186/s40537-023-00798-1openalex-snapshot

Big data: an optimized approach for cluster initialization

inferred← citeshttps://doi.org/10.3390/app14103956openalex-snapshot

Optimization and Application of XGBoost Logging Prediction Model for Porosity and Permeability Based on K-means Method

inferred← citeshttps://doi.org/10.3390/electronics10243124openalex-snapshot

A Method for Class-Imbalance Learning in Android Malware Detection

inferred← citeshttps://doi.org/10.4171/msl/21openalex-snapshot

Statistical learning guarantees for compressive clustering and compressive mixture modeling

inferred← citeshttps://doi.org/10.70675/a4882bb8z7781z460cz9b92z9bf3ccc853bbopenalex-snapshot

Echantillonnage pour l'accélération des méthodes à noyaux et sélection gloutonne pour les représentations parcimonieuses

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.