arXiv (All)
open access
Latent class analysis by regularized spectral clustering
The latent class model is a highly effective tool in the analysis of categorical data from social, psychological, and behavioral sciences, where populations often share hidden common characteristics. In this article, we introduce two new algorithms for estimating the parameters of a latent class mod
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
- The Proposed Clustering Model to Predict Autism Spectrum Disorder in Toddlers.#402602
- Semi-Supervised Classification with Informative Missing Labels in Weibull Mixture Models#820785
- Discovering Latent Groups for Robust Classification#299931
- Salient Bayesian Clustering for Proportional Data via a Multivariate Beta Mixture Model#1122531
- SSLfmm: An R Package for Semi-Supervised Learning with Mixed Missingness#1155082
Record · ID 972849
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