Name
Abstract | ||
|---|---|---|
We apply methods for selecting subsets of dimensions from high-dimensional score spaces, and subsets of data for training, using submodular function optimization. Submodular functions provide theoretical performance guarantees while simultaneously retaining extremely fast and scalable optimization via an accelerated greedy algorithm. We evaluate this approach on two applications: data subset selection for phone recognizer training, and semi-supervised learning for phone segment classification. Interestingly, the first application uses submodularity twice: first for score space sub-selection and then for data subset selection. Our approach is computationally efficient but still consistently outperforms a number of baseline methods. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICASSP.2013.6639057 | 2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
feature selection, Fisher kernel, acoustic similarity, graph-based learning, submodularity | Speech processing,Feature selection,Pattern recognition,Computer science,Submodular set function,Greedy algorithm,Phone,Signal classification,Artificial intelligence,Machine learning,Scalability | Conference |
ISSN | Citations | PageRank |
1520-6149 | 16 | 0.62 |
References | Authors | |
12 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuzong Liu | 1 | 96 | 6.63 |
| Kai Wei | 2 | 143 | 9.34 |
| Katrin Kirchhoff | 3 | 1026 | 95.24 |
| Yisong Song | 4 | 18 | 1.00 |
| Jeff Bilmes | 5 | 3420 | 289.94 |