Identification of NMF by choosing maximum-volume basis vectors
Identification of NMF by choosing maximum-volume basis vectors explores A new NMF framework that makes basis vectors as distinct as possible to improve interpretability and handle highly mixed data.. Commercial viability score: 3/10 in Matrix Factorization.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Prior WorkA Stable Neural Statistical Dependence Estimator for Autoencoder Feature AnalysisViability: 3.0
- Prior WorkDiffusion-Based Feature Denoising and Using NNMF for Robust Brain Tumor ClassificationViability: 3.0
- Higher ViabilityMM-algorithms for traditional and convex NMF with Tweedie and Negative Binomial cost functions and empirical evaluationViability: 7.0
- Higher ViabilityMLOW: Interpretable Low-Rank Frequency Magnitude Decomposition of Multiple Effects for Time Series ForecastingViability: 7.0
- Higher Viability
Related Resources
- matrix factorization(glossary)