using ProjectiveDictionaryPairLearning dpldemo()
The output should be something similar to this
The running time for DPL training is : 6.79 s The running time for DPL testing is : 0.23 s Recognition rate for DPL is : 97.579%
Comparison with MATLAB version
Actually the MATLAB version runs much faster as below.
The running time for DPL training is : 3.12 s The running time for DPL testing is : 0.17 s Recognition rate for DPL is : 0.976
Prepare Data and Labels
- Training Data
TrData: Column Vectors (Should be
- Training Label
TrLabel: Class Label Integers in A Row
- Testing Data
TtData: Column Vectors (Should also be
- Testing Label
TtLabel: Class Label Integers in A Row
DictSize = 30 τ = 0.05 λ = 0.003 γ = 0.0001
DictMat, EncoderMat = TrainDPL(TrData, TrLabel, DictSize, τ, λ, γ)
PredictLabel, Error, Distance = ClassificationDPL(TtData, DictMat, EncoderMat, DictSize)
Inner functions have been exposed to use.
DataMat, D, P, DataInvMat, A = initialization(Data, Label, DictSize, τ, λ, γ)
updateA!(A, D, DataMat, P, τ, DictSize)
updateD!(D, A, DataMat)
updateP!(P, A, DataInvMat, DataMat, τ)
The original matlab code is for the paper:
Shuhang Gu, Lei Zhang, Wangmeng Zuo, and Xiangchu Feng, "Projective Dictionary Pair Learning for Pattern Classification," In NIPS 2014.
From “dictionary learning” to “dictionary pair learning”!
The julia port of this code is for the paper:
Qu, Xiaofeng; Zhang, David; Lu, Guangming; and Guo, Zhenhua, “Door knob hand recognition system,” Will appear in Systems, Man, and Cybernetics: Systems, IEEE Transactions on.
The example feature dataset (YaleB_Jiang) used in this code is from Dr. Zhuolin Jiang: http://www.umiacs.umd.edu/~zhuolin/projectlcksvd.html.
For the experiments on AR and caltech 101 dataset, we also used the feature datasets provided by Dr. Jiang.
For experiment on UCF50, we used the Action bank feature provided in: http://www.cse.buffalo.edu/~jcorso/r/actionbank/. Please refer to our paper for the detailed experimental setting.
If you have problems with the julia code, please contact us at email@example.com.
- A thank you to afternone, who optimized the julia code
- NEW add more tests
Optimize the performance
- NEW optimize the performance further