All software related to the AsyDiL project can be found at https://gitlab.cs.pub.ro/asydil. The list below contains the programs associated with our publications.
- Programs for paper “Atom dimension adaptation for infinite set dictionary learning”, by Andra Băltoiu, Denis C. Ilie-Ablachim, and Bogdan Dumitrescu, 2024: programs
- Programs for paper “Sparse representations with Gaussian atoms”, IEEE Open Journal on Signal Processing, 2024, by Denis C. Ilie-Ablachim, Andra Băltoiu and Bogdan Dumitrescu: MATLAB programs, Python programs, data
- MATLAB programs for paper “Dictionary learning with cone atoms and application to anomaly detection”, Signal Processing, June 2024, by Andra Băltoiu, Denis C. Ilie-Ablachim and Bogdan Dumitrescu: programs, data
- MATLAB programs for paper “Classification with dictionary learning and a distance barrier promoting incoherence”, MLSP 2023, by Denis C. Ilie-Ablachim and Bogdan Dumitrescu: zip file
- MATLAB programs for paper “Sparse representations with cone atoms”, ICASSP 2023, by Denis C. Ilie-Ablachim, Andra Băltoiu, and Bogdan Dumitrescu: zip file
- MATLAB programs for paper “Incoherent frames design and dictionary learning using a distance barrier”, Signal Processing, by Denis C. Ilie-Ablachim and Bogdan Dumitrescu.
- Python programs for paper “Haar-Laplacian for directed graphs”, by Theodor Adrian Badea, Bogdan Dumitrescu, zip file
- Python programs for paper “Community-Augmented Local-Link Intensity: a score for anomaly detection in graphs”, by Theodor Adrian Badea, Bogdan Dumitrescu, zip file
- Python programs for paper “i-DGCN: A Spectral Convolutional Network For Directed Graphs Using An Intensity Laplacian”, by Theodor Adrian Badea, Bogdan Dumitrescu, zip file
- Python programs for paper “Online Computation of Reduced Egonet Features for Anomaly Detection in Bank Transactions Graphs”, by Cristian-Enache Zica, Bogdan Dumitrescu, zip file
- Python programs for paper “Angle Based Dictionary Learning for Outlier Detection”, by Denis C. Ilie-Ablachim, Bogdan Dumitrescu: zip file