Name
Abstract | ||
|---|---|---|
This paper proposes a new mean-shifting Incremental PCA (IPCA) method based on the autocorrelation matrix. The dimension of the updated matrix remains constant instead of increasing with the number of input data points. Comparing to some previous batch and iterative PCA algorithms, the proposed IPCA requires lower computational time and storage capacity owing to the two transformations designed. The experiment results show the efficiency and accuracy of the proposed IPCA method in applications of the on-line visual learning and recognition. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/s11063-009-9117-1 | Neural Processing Letters |
Keywords | Field | DocType |
Principal component analysis,Incremental updating,On-line visual learning,Object recognition | Data point,Batch production,Pattern recognition,Iterative method,Autocorrelation matrix,Algorithm,Visual learning,Batch processing,Artificial intelligence,Principal component analysis,Mathematics,Cognitive neuroscience of visual object recognition | Journal |
Volume | Issue | ISSN |
30 | 3 | 1370-4621 |
Citations | PageRank | References |
7 | 0.45 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Huang | 1 | 163 | 14.20 |
| Zhang Yi | 2 | 1765 | 194.41 |
| Xiaorong Pu | 3 | 85 | 11.17 |