Dataset

name	domain	instances	nominal	numeric	labels	cardinality	density	distinct
yeast	biology	2417	0	103	14	4.237	0.303	198

Performance

evaluate	paper	this code
image data
One-error	0.242±0.033	0.2490±0.0310
Hamming loss	0.144±0.012	0.1487±0.0120
Coverage	0.156±0.016	0.1603±0.0083
Ranking loss	0.128±0.013	0.1324±0.0133
Average precision	0.843±0.018	0.8374±0.0172
Macro-averaging F1	0.660±0.030	0.6528±0.0281
Micro-averaging F1	0.659±0.031	0.6495±0.0285
yeast data
One-error	0.218±0.027	0.2321±0.0259
Hamming loss	0.190±0.005	0.1926±0.0074
Coverage	0.446±0.010	0.4475±0.0129
Ranking loss	0.162±0.007	0.1639±0.0089
Average precision	0.775±0.013	0.7698±0.0159
Macro-averaging F1	0.411±0.018	0.4041±0.0167
Micro-averaging F1	0.655±0.010	0.6503±0.0167
scene data
One-error	0.175±0.027	0.1803±0.0121
Hamming loss	0.072±0.009	0.0755±0.0038
Coverage	0.062±0.006	0.0654±0.0042
Ranking loss	0.058±0.005	0.0608±0.0043
Average precision	0.897±0.012	0.8926±0.0070
Macro-averaging F1	0.787±0.023	0.7712±0.0115
Micro-averaging F1	0.780±0.026	0.7642±0.0141
enron data
One-error	0.207±0.038	0.2203±0.0127
Hamming loss	0.045±0.003	0.0456±0.0009
Coverage	0.239±0.028	0.2489±0.0131
Ranking loss	0.079±0.028	0.0837±0.0060
Average precision	0.718±0.025	0.7115±0.0066
Macro-averaging F1	0.325±0.044	0.2479±0.0185
Micro-averaging F1	0.580±0.023	0.5705±0.0065

Parameter

• lamda for get label correlation matrix : $\frac { 1 } { 100 } \left| \mathbf { Y } _ { j } ^ { \top } \mathbf { Y } _ { - j } \right| _ { \infty }$
• alpha for CAMEL: [0, 0.1, 0.2,...,1]
• alpha ban for ADMM: [0, 0.1, 0.2,...,1]
• lamda1: 1
• lamda2: [0.001, 0.002, 0.01, 0.02, 0.1, 0.2, 1]
• rho(ADMM the augmented lagrangian parameter): not mention in the paper, set 1
• 5 fold cross validation

Requrements

• scikit-learn 0.19.1
• numpy 1.16.2
• cupy-cuda100 5.3.0

Usage

• prepare data
• run main

Thanks

ADMM tutorial(The alternating direction method of multipliers)交替方向乘子法 https://web.stanford.edu/~boyd/papers/admm/lasso/lasso.html the author's code

Reference

Collaboration based Multi-Label Learning 2019 AAAI