Minimal Learning Machine for Multi-Label Learning

MATLAB implementations of Minimal Learning Machine (MLM) approaches and metrics for multi-label classification.

Sample script for training and testing

To run training and testing for the approaches see run_ml_mlm_demo.m. This script uses a synthetic dataset.

Training

• Distance regression training (MLM training) dist_reg_train.m
• LOOCV with Ranking Loss statistic for ML-MLM ml_mlm_loocv_train.m

Predict

• Nearest Neighbour MLM (NN-MLM) nn_mlm_pred.m
• Localization Linear System MLM (LLS-MLM) lls_mlm_pred.m
• Cubic equations MLM (C-MLM) cubic_mlm_pred.m
• Multi-Label MLM ml_mlm_pred.m

Thresholding

• Local Rcut thresholding local_rcut.m

Metrics

• Compute all metric results: compute_metrics.m (see run_ml_mlm_demo.m for examples)
• Ranking: ranking_loss.m, average_precision.m, coverage.m, one_error.m, precision_at_k.m
• Bipartition: accuracy.m, hamming_loss.m, macro_f1, micro_f1, micro_recall, micro_precision