arXiv (All)
open access
Iterative Exploration-Driven Sparse SDP Clustering via Thompson Sampling
High-dimensional sparse clustering is a challenging combinatorial problem due to the tight coupling between cluster assignment and variable selection. We demonstrate that semidefinite programming (SDP) relaxation of K-means is robust to variable over-selection by establishing minimax separation boun
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Multi-ResNets for Subspace Preconditioning in Constrained Optimization#259525
- LOW-RANK AUGMENTED LAGRANGIAN METHODS FOR SDP RELAXATIONS#631196
- Learning to Bias: Machine Learning-Enhanced Particle Filters#1104697
- Diversity-Aware Batch-Mode Active Learning for Efficient Sampling in Data-Driven Constitutive Modeling#1083094
- High-Magnetization Sampling at Low Temperatures: Ising Models and Bayesian Sparse Linear Regression#986498
Record · ID 1010999
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive