Name
Abstract | ||
|---|---|---|
It was shown recently that the K L1-norm principal components (L1-PCs) of a real-valued data matrix X ∈ R D×N (N data samples of D dimensions) can be exactly calculated with cost O(2NK) or, when advantageous, O(NdK - K + 1) where d=rank (X), K<;d. In applications where X is large (e.g., “big” data of large N and/or “heavy” data of large d), these costs are prohibitive. In this paper, we present a ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TSP.2017.2708023 | IEEE Transactions on Signal Processing |
Keywords | DocType | Volume |
Signal processing algorithms,Approximation algorithms,Principal component analysis,Complexity theory,Algorithm design and analysis,Computational efficiency,Standards | Journal | 65 |
Issue | ISSN | Citations |
16 | 1053-587X | 3 |
PageRank | References | Authors |
0.40 | 20 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Panos P. Markopoulos | 1 | 45 | 9.35 |
| Sandipan Kundu | 2 | 40 | 4.49 |
| Shubham Chamadia | 3 | 3 | 3.78 |
| Dimitris Pados | 4 | 208 | 26.49 |