Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105591
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Guo, Y | - |
dc.creator | Chung, F | - |
dc.creator | Li, G | - |
dc.creator | Wang, J | - |
dc.creator | Gee, JC | - |
dc.date.accessioned | 2024-04-15T07:35:14Z | - |
dc.date.available | 2024-04-15T07:35:14Z | - |
dc.identifier.issn | 1556-4681 | - |
dc.identifier.uri | http://hdl.handle.net/10397/105591 | - |
dc.language.iso | en | en_US |
dc.publisher | Association for Computing Machinery | en_US |
dc.rights | ©2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Knowledge Discovery from Data, http://dx.doi.org/10.1145/3319911. | en_US |
dc.subject | Label specific features | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Multi-label learning | en_US |
dc.title | Leveraging label-specific discriminant mapping features for multi-label learning | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 13 | - |
dc.identifier.issue | 2 | - |
dc.identifier.doi | 10.1145/3319911 | - |
dcterms.abstract | As an important machine learning task, multi-label learning deals with the problem where each sample instance (feature vector) is associated with multiple labels simultaneously. Most existing approaches focus on manipulating the label space, such as exploiting correlations between labels and reducing label space dimension, with identical feature space in the process of classification. One potential drawback of this traditional strategy is that each label might have its own specific characteristics and using identical features for all label cannot lead to optimized performance. In this article, we propose an effective algorithm named LSDM, i.e., leveraging label-specific discriminant mapping features for multi-label learning, to overcome the drawback. LSDM sets diverse ratio parameter values to conduct cluster analysis on the positive and negative instances of identical label. It reconstructs label-specific feature space which includes distance information and spatial topology information. Our experimental results show that combining these two parts of information in the new feature representation can better exploit the clustering results in the learning process. Due to the problem of diverse combinations for identical label, we employ simplified linear discriminant analysis to efficiently excavate optimal one for each label and perform classification by querying the corresponding results. Comparison with the state-of-the-art algorithms on a total of 20 benchmark datasets clearly manifests the competitiveness of LSDM. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | ACM transactions on knowledge discovery from data, Apr. 2019, v. 13, no. 2, 24 | - |
dcterms.isPartOf | ACM transactions on knowledge discovery from data | - |
dcterms.issued | 2019-04 | - |
dc.identifier.scopus | 2-s2.0-85065715619 | - |
dc.identifier.eissn | 1556-472X | - |
dc.identifier.artn | 24 | - |
dc.description.validate | 202402 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | COMP-0632 | en_US |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Natural Science Foundation of China; GRF; Tongji University; National Key R&D Program of China; Central public welfare research institutes | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 21046876 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Guo_Leveraging_Label-Specific_Discriminant.pdf | Pre-Published version | 4.8 MB | Adobe PDF | View/Open |
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