On the use of adversarial validation for quantifying dissimilarity in geospatial machine learning prediction

no code implementations • 19 Apr 2024 • Yanwen Wang, Mahdi Khodadadzadeh, Raul Zurita-Milla

The proposed method is based on adversarial validation, which is an approach that can check whether sample data and prediction locations can be separated with a binary classifier.

Hierarchical Sparse Subspace Clustering (HESSC): An Automatic Approach for Hyperspectral Image Analysis

1 code implementation • 28 Jul 2020 • Kasra Rafiezadeh Shahi, Mahdi Khodadadzadeh, Laura Tusa, Pedram Ghamisi, Raimon Tolosana-Delgado, and Richard Gloaguen

In addition, in order to have a comparison with conventional clustering algorithms, HESSC’s performance is compared with K-means and FCM.

BIG-bench Machine Learning Clustering +2