Name
Abstract | ||
|---|---|---|
In this paper, we propose the multi-domain dictionary learning (MDDL) to make dictionary learning-based classification more robust to data representing in different domains. We use adversarial neural networks to generate data in different styles, and collect all the generated data into a miscellaneous dictionary. To tackle the dictionary learning with many samples, we compute the weighting matrix that compress the miscellaneous dictionary from multi-sample per class to single sample per class. We show that the time complexity solving the proposed MDDL with weighting matrix is the same as solving the dictionary with single sample per class. Moreover, since the weighting matrix could help the solver rely more on the training data, which possibly lie in the same domain with the testing data, the classification could be more accurate. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/GlobalSIP45357.2019.8969434 | 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) |
Keywords | Field | DocType |
Dictionary learning,sparse representation-based classification,multi-domain image classification | Weighting,Dictionary learning,Matrix (mathematics),Computer science,Multi domain,Test data,Artificial intelligence,Solver,Time complexity,Artificial neural network,Machine learning | Conference |
Volume | ISSN | ISBN |
abs/1811.00274 | 2376-4066 | 978-1-7281-2724-8 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cho-Ying Wu | 1 | 3 | 2.75 |
| Ulrich Neumann | 2 | 26 | 3.60 |