Handling ambiguity via input-output kernel learning
Data ambiguities exist in many data mining and machine learning applications such as text categorization and image retrieval. For instance, it is generally beneficial to utilize the ambiguous unlabeled documents to learn a more robust classifier for text categorization under the semi-supervised lear...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/99740 http://hdl.handle.net/10220/13014 |
| _version_ | 1826129501234397184 |
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| author | Xu, Xinxing Tsang, Ivor Wai-Hung Xu, Dong |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Xu, Xinxing Tsang, Ivor Wai-Hung Xu, Dong |
| author_sort | Xu, Xinxing |
| collection | NTU |
| description | Data ambiguities exist in many data mining and machine learning applications such as text categorization and image retrieval. For instance, it is generally beneficial to utilize the ambiguous unlabeled documents to learn a more robust classifier for text categorization under the semi-supervised learning setting. To handle general data ambiguities, we present a unified kernel learning framework named Input-Output Kernel Learning (IOKL). Based on our framework, we further propose a novel soft margin group sparse Multiple Kernel Learning (MKL) formulation by introducing a group kernel slack variable to each group of base input-output kernels. Moreover, an efficient block-wise coordinate descent algorithm with an analytical solution for the kernel combination coefficients is developed to solve the proposed formulation. We conduct comprehensive experiments on benchmark datasets for both semi-supervised learning and multiple instance learning tasks, and also apply our IOKL framework to a computer vision application called text-based image retrieval on the NUS-WIDE dataset. Promising results demonstrate the effectiveness of our proposed IOKL framework. |
| first_indexed | 2024-10-01T07:41:38Z |
| format | Conference Paper |
| id | ntu-10356/99740 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:41:38Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/997402020-05-28T07:17:50Z Handling ambiguity via input-output kernel learning Xu, Xinxing Tsang, Ivor Wai-Hung Xu, Dong School of Computer Engineering IEEE International Conference on Data Mining (12th : 2012 : Brussels, Belgium) DRNTU::Engineering::Computer science and engineering Data ambiguities exist in many data mining and machine learning applications such as text categorization and image retrieval. For instance, it is generally beneficial to utilize the ambiguous unlabeled documents to learn a more robust classifier for text categorization under the semi-supervised learning setting. To handle general data ambiguities, we present a unified kernel learning framework named Input-Output Kernel Learning (IOKL). Based on our framework, we further propose a novel soft margin group sparse Multiple Kernel Learning (MKL) formulation by introducing a group kernel slack variable to each group of base input-output kernels. Moreover, an efficient block-wise coordinate descent algorithm with an analytical solution for the kernel combination coefficients is developed to solve the proposed formulation. We conduct comprehensive experiments on benchmark datasets for both semi-supervised learning and multiple instance learning tasks, and also apply our IOKL framework to a computer vision application called text-based image retrieval on the NUS-WIDE dataset. Promising results demonstrate the effectiveness of our proposed IOKL framework. 2013-08-05T06:45:10Z 2019-12-06T20:10:54Z 2013-08-05T06:45:10Z 2019-12-06T20:10:54Z 2012 2012 Conference Paper https://hdl.handle.net/10356/99740 http://hdl.handle.net/10220/13014 10.1109/ICDM.2012.105 en |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Xu, Xinxing Tsang, Ivor Wai-Hung Xu, Dong Handling ambiguity via input-output kernel learning |
| title | Handling ambiguity via input-output kernel learning |
| title_full | Handling ambiguity via input-output kernel learning |
| title_fullStr | Handling ambiguity via input-output kernel learning |
| title_full_unstemmed | Handling ambiguity via input-output kernel learning |
| title_short | Handling ambiguity via input-output kernel learning |
| title_sort | handling ambiguity via input output kernel learning |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/99740 http://hdl.handle.net/10220/13014 |
| work_keys_str_mv | AT xuxinxing handlingambiguityviainputoutputkernellearning AT tsangivorwaihung handlingambiguityviainputoutputkernellearning AT xudong handlingambiguityviainputoutputkernellearning |