dataset

dataset	labels	instances	feature	cardinality
mirflickr	38	25000	1000	4.7

Performance

type	dataset	Micro F1	Macro F1
this code	mirflickr	0.540699	0.39643
paper	mirflickr	0.54	0.39

Parameter

• alpha(for output loss para): 0.5
• learning rate: 0.0001
• learning rate decay: 0.98
• momentum: 0.99
• optimizer decay: 0.9
• l2penalty：0.001
• maxepoch: 50
• lagrange para:0.5
• batch size: 500
• hidden units: 512
• latent embedding units: (0, 1) of label dims, default 0.8

Description

• learning rate: decay by new_lr = lr_init * decay^epoch

• custom optimizer: similar to RMSProp, decay 0.9, momentum 0.99

  init rrr = 0, delta = 0 rrr=sqrt((rrr.^2)0.9+(grad.^2)0.1) grad=grad/rrr delta=momentumdelta-etagrad new_weight=old_weight + delta

• lagrange para: for caculate embedding loss

Requrements

• tensorflow 1.12.0
• numpy 1.16.2

Thanks

the paper's author code matlab https://github.com/chihkuanyeh/C2AE

other implement(I think there some mistakes in this code) https://github.com/dhruvramani/C2AE-Multilabel-Classification

Reference

Yeh, C. K., Wu, W. C., Ko, W. J., & Wang, Y. C. F. (2017). Learning deep latent space for multi-label classification. In AAAI (pp. 2838–2844).