Name
Abstract | ||
|---|---|---|
In this paper, we propose a view composition method for co-training. In order to compose views properly, two assumptions should be satisfied. One is that two views are class-conditionally independent on each other; the other is that the classification information between labels and view is high. We apply Class-Conditional Independent Component Analysis (CC-ICA) to obtain new features which are mutually independent, and compose views hold a high classification information. We show that our method is promising and effective through the experiment. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SCIS-ISIS.2014.7044858 | SCIS&ISIS |
Keywords | Field | DocType |
independent component analysis,learning (artificial intelligence),pattern classification,cc-ica,automatic view composition method,class-conditional independent component analysis,classification information,co-training,mutual information,view composition | Data mining,Computer science,Co-training,Mutual information,Artificial intelligence,Independent component analysis,Machine learning,Independence (probability theory) | Conference |
ISSN | Citations | PageRank |
2377-6870 | 0 | 0.34 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| HyeWoo Lee | 1 | 2 | 0.72 |
| Kyoungmin Kim | 2 | 0 | 1.69 |
| Jae-Dong Lee | 3 | 175 | 26.07 |
| Jee-Hyong Lee | 4 | 316 | 49.65 |