Name
Abstract | ||
|---|---|---|
Estimating the number of correlated components between two data sets is a challenging task in the case of small sample support. Typically, a rank-reduction preprocessing step based on principal component analysis (PCA) is carried out on each data set individually to reduce the dimensionality before analyzing correlation between the data sets. However, PCA retains the components with the largest variance within a data set, and therefore fails when these components are not the ones that account for the correlation between the data sets. To overcome this, we propose an alternative technique that, instead of projecting the data into a single subspace, uses a large number of random projections. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8683177 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
Correlation analysis, small sample support, random projection, KL divergence | Random projection,Data set,Pattern recognition,Subspace topology,Computer science,Curse of dimensionality,Preprocessor,Correlation,Artificial intelligence,Principal component analysis,Kullback–Leibler divergence | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Lameiro | 1 | 39 | 7.71 |
| Tanuj Hasija | 2 | 1 | 1.03 |
| Tim Marrinan | 3 | 7 | 2.55 |
| Peter J. Schreier | 4 | 10 | 4.26 |