Name
Abstract | ||
|---|---|---|
Assume our data consists of unit vectors (directions) and we are to find a small orthogonal set of the “the most important directions” summarizing the data. We develop online algorithms for this type of problem. The techniques used are similar to Principal Component Analysis which finds the most important small rank subspace of the data.The new problem is significantly more complex since the online algorithm maintains uncertainty over the most relevant subspace as well as directional information. |
Year | Venue | Field |
|---|---|---|
2013 | COLT | Data mining,Online algorithm,Subspace topology,Computer science,Artificial intelligence,Principal component analysis,Machine learning,Unit vector |
DocType | Citations | PageRank |
Conference | 1 | 0.39 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wouter M. Koolen | 1 | 87 | 17.35 |
| Jiazhong Nie | 2 | 45 | 4.72 |
| Manfred K. Warmuth | 3 | 6105 | 1975.48 |