Joint auto-weighted graph fusion for scalable semi-supervised learning (JAWGF)

Python implementation of the Joint Auto-Weighted Graph Fusion method with Flexible Manifold Embedding as described in [0]

Quick start

Installation

pip install JAWGF

Implementation Example

from JAWGF import joint_fusion, predict

F, Q, b = joint_fusion([X_view1, X_view2], Y, K=15)

y_soft_new = predict(new_sample, Q, b)
y_pred = y_soft_new.argmax()

Method overview

Method overview from Bahrami, Dornaika, and Bosaghzadeh (2021) [0].

Citation information

Please cite [0] when using JAWGF in your research and reference the appropriate release version.

Publications

[0] Bahrami, Saeedeh, Fadi Dornaika, and Alireza Bosaghzadeh. "Joint auto-weighted graph fusion and scalable semi-supervised learning." Information Fusion 66 (2021): 213-228.