Tensor Robust Principal Component Analysis via Non-Convex Low Rank Approximation
Tensor Robust Principal Component Analysis (TRPCA) plays a critical role in handling high multi-dimensional data sets, aiming to recover the low-rank and sparse components both accurately and efficiently. In this paper, different from current approach, we developed a new t-Gamma tensor quasi-norm as...
| 出版年: | Applied Sciences |
|---|---|
| 主要な著者: | , , , , |
| フォーマット: | 論文 |
| 言語: | 英語 |
| 出版事項: |
MDPI AG
2019-04-01
|
| 主題: | |
| オンライン･アクセス: | https://www.mdpi.com/2076-3417/9/7/1411 |
| 要約: | Tensor Robust Principal Component Analysis (TRPCA) plays a critical role in handling high multi-dimensional data sets, aiming to recover the low-rank and sparse components both accurately and efficiently. In this paper, different from current approach, we developed a new t-Gamma tensor quasi-norm as a non-convex regularization to approximate the low-rank component. Compared to various convex regularization, this new configuration not only can better capture the tensor rank but also provides a simplified approach. An optimization process is conducted via tensor singular decomposition and an efficient augmented Lagrange multiplier algorithm is established. Extensive experimental results demonstrate that our new approach outperforms current state-of-the-art algorithms in terms of accuracy and efficiency. |
|---|---|
| ISSN: | 2076-3417 |