Name
Title | ||
|---|---|---|
A family of fuzzy learning algorithms for robust principal component analysis neural networks |
Abstract | ||
|---|---|---|
In this paper, we analyze Xu and Yuille's robust principal component analysis (RPCA) learning algorithms by means of the distance measurement in space. Based on the analysis, a family of fuzzy RPCA learning algorithms is proposed, which is robust against outliers. These algorithms can explicitly be understood from the viewpoint of fuzzy set theory, though Xu and Yuille's algorithms were proposed based on a statistical physics approach. In the proposed algorithms, an adaptive learning procedure overcomes the difficulty of selection of learning parameters in Xu and Yuille's algorithms. Furthermore, the robustness of proposed algorithms is investigated by using the theory of influence functions. Simulations are carried out to illustrate the robustness of these algorithms. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TFUZZ.2009.2038711 | IEEE T. Fuzzy Systems |
Keywords | Field | DocType |
principal component analysis,neural nets,neural network,algorithm design and analysis,fuzzy set theory,statistical physics,robustness,physics,neural networks,set theory,adaptive learning | Fuzzy logic,Outlier,Algorithm,Robust principal component analysis,Fuzzy set,Robustness (computer science),Artificial intelligence,Artificial neural network,Adaptive learning,Mathematics,Machine learning,Principal component analysis | Journal |
Volume | Issue | ISSN |
18 | 1 | 1063-6706 |
Citations | PageRank | References |
22 | 0.74 | 27 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Cheng Lv | 1 | 337 | 54.52 |
| Kok Kiong Tan | 2 | 923 | 99.57 |
| Zhang Yi | 3 | 1765 | 194.41 |
| Su-Nan Huang | 4 | 505 | 61.65 |