Name
Abstract | ||
|---|---|---|
•We propose an L1-norm principal-component analysis based robust subspace tracking method to capture the intrinsic low-rank structure of streaming data in the presence of outliers.•The proposed L1-subspace tracking method updates the subspace at each time slot with new sensor datum, utilizing the subspace obtained at the previous time slot, and a small batch of most recent data samples.•The proposed method has the merits of data outlier suppression through sample weighting and speed acceleration through a warm-start bit-flipping technique, as demonstrated in various application fields.•The proposed method offers superior subspace estimation accuracy compared to state-of-the-art subspace tracking methods, and is comparable to existing methods in terms of computational complexity and execution time. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.patcog.2019.106992 | Pattern Recognition |
Keywords | DocType | Volume |
Dimensionality reduction,Eigenvector decomposition,Internet-of-Things,L1-norm,Outliers,Principal-component analysis,Subspace learning | Journal | 97 |
Issue | ISSN | Citations |
1 | 0031-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Liu | 1 | 60 | 5.05 |
| konstantinos tountas | 2 | 15 | 2.51 |
| Dimitris Pados | 3 | 208 | 26.49 |
| Stella N. Batalama | 4 | 465 | 37.92 |
| Michael J. Medley | 5 | 337 | 26.06 |