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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wang Ziqiang Sun Xia |
| Copyright Year | 2009 |
| Description | Author affiliation: School of Information Science and Engineering, Henan University of Technology, Zhengzhou (Wang Ziqiang; Sun Xia) |
| Abstract | To efficiently deal with high dimensionality and precision problems in document retrieval, a novel document retrieval algorithm based on manifold learning and ant colony optimization(ACO) algorithm is proposed. The high-dimensional document data are first projected into lower-dimensional feature space with neighborhood preserving embedding (NPE) algorithm, the ACO algorithm is then applied to retrieve relevant documents in the reduced lower-dimensionality document feature space. Extensive experiments on real-world data set demonstrate the effectiveness of the proposed algorithm. |
| Starting Page | 152 |
| Ending Page | 155 |
| File Size | 78553 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424445905 |
| DOI | 10.1109/ICBNMT.2009.5348468 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-10-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Ant colony optimization Scattering Information retrieval Large scale integration Pattern recognition Manifold learning Sun Manifolds Document retrieval Space technology Feedback Ant colony optimization(ACO) Neighborhood preserving embedding(NPE) Linear discriminant analysis |
| Content Type | Text |
| Resource Type | Article |
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