Name
Abstract | ||
|---|---|---|
In this paper, we propose a general model to address the overfitting problem in online similarity learning for big data, which is generally generated by two kinds of redundancies: 1) feature redundancy, that is there exists redundant (irrelevant) features in the training data; 2) rank redundancy, that is non-redundant (or relevant) features lie in a low rank space. To overcome these, our model is ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TBDATA.2017.2688360 | IEEE Transactions on Big Data |
Keywords | Field | DocType |
Measurement,Redundancy,Mathematical model,Data models,Optimization,Computational modeling,Robustness | Similarity learning,Online algorithm,Data mining,Feature selection,Computer science,Regularization (mathematics),Redundancy (engineering),Artificial intelligence,Overfitting,Singular value decomposition,Pattern recognition,Matrix norm,Machine learning | Journal |
Volume | Issue | ISSN |
4 | 1 | 2332-7790 |
Citations | PageRank | References |
4 | 0.40 | 0 |
Authors | ||
6 | ||