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Found 6 papers, 6 papers with code
MLXP: A framework for conducting replicable Machine Learning eXperiments in Python
Replicability in machine learning (ML) research is increasingly concerning due to the utilization of complex non-deterministic algorithms and the dependence on numerous hyper-parameter choices, such as model architecture and training datasets.
Fast Semi-supervised Unmixing using Non-convex Optimization
Our experimental results validate that enforcing the convexity constraint outperforms the sparsity prior for the endmember library.
Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package
Additionally, we draw a critical comparison between advanced and conventional techniques from the three categories.
SUnAA: Sparse Unmixing using Archetypal Analysis
Unlike most conventional sparse unmixing methods, here the minimization problem is non-convex.
Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing
In this paper, we introduce a new algorithm based on archetypal analysis for blind hyperspectral unmixing, assuming linear mixing of endmembers.
A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration
Hyperspectral imaging offers new perspectives for diverse applications, ranging from the monitoring of the environment using airborne or satellite remote sensing, precision farming, food safety, planetary exploration, or astrophysics.
