Name
Abstract | ||
|---|---|---|
Several data analysis tools such as (kernel) canonical correlation analysis and various multi-view learning methods require paired observations in two data sets. We study the problem of inferring such pairing for data sets with no known one-to-one pairing. The pairing is found by an iterative algorithm that alternates between searching for feature representations that reveal statistical dependencies between the data sets, and finding the best pairs for the samples. The method is applied on pairing probe sets of two different microarray platforms. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICASSP.2009.4959895 | ICASSP |
Keywords | Field | DocType |
data analysis,iterative methods,learning (artificial intelligence),statistical analysis,data analysis tools,feature representations,iterative algorithm,kernel canonical correlation analysis,microarray platforms,multiview learning,one-to-one pairing,pairing probe sets,statistical dependency,canonical correlation,co-occurrence data,dependency,multi-view learning | Kernel (linear algebra),Analysis tools,Data set,Pattern recognition,Canonical correlation,Iterative method,Computer science,Pairing,Correlation,Artificial intelligence,Probability density function | Conference |
ISSN | Citations | PageRank |
1520-6149 | 2 | 0.40 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abhishek Tripathi | 1 | 60 | 6.35 |
| Arto Klami | 2 | 412 | 34.43 |
| Samuel Kaski | 3 | 2755 | 245.52 |