Learning with Augmented Multi-Instance View

Yue Zhu, Jianxin Wu, Yuan Jiang, Zhi-Hua Zhou
Proceedings of the Sixth Asian Conference on Machine Learning, PMLR 39:234-249, 2015.

Abstract

In this paper, we propose the Augmented Multi-Instance View (AMIV) framework to construct a better model by exploiting augmented information. For example, abstract screening tasks may be difficult because only abstract information is available, whereas the performance can be improved when the abstracts of references listed in the document can be exploited as augmented information. If each abstract is represented as an instance (i.e., a feature vector) x, then with the augmented information, it can be represented as an instance-bag pair (x;B), where B is a bag of instances (i.e., the abstracts of references). Note that if x has a label y, then we assume that there must exist at least one instance in the bag B having the label y. We regard x and B as two views, i.e., a single-instance view augmented with a multi-instance view, and propose the AMIV-lss approach by establishing a latent semantic subspace between the two views. The AMIV framework can be applied when the augmented information is presented as multi-instance bags and to the best of our knowledge, such a learning with augmented multi-instance view problem has not been touched before. Experimental results on twelve TechPaper datasets, five PubMed data sets and a WebPage data set validate the effectiveness of our AMIV-lss approach.

Cite this Paper

BibTeX
@InProceedings{pmlr-v39-zhu14, title = {Learning with Augmented Multi-Instance View}, author = {Zhu, Yue and Wu, Jianxin and Jiang, Yuan and Zhou, Zhi-Hua}, booktitle = {Proceedings of the Sixth Asian Conference on Machine Learning}, pages = {234--249}, year = {2015}, editor = {Phung, Dinh and Li, Hang}, volume = {39}, series = {Proceedings of Machine Learning Research}, address = {Nha Trang City, Vietnam}, month = {26--28 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v39/zhu14.pdf}, url = {https://proceedings.mlr.press/v39/zhu14.html}, abstract = {In this paper, we propose the Augmented Multi-Instance View (AMIV) framework to construct a better model by exploiting augmented information. For example, abstract screening tasks may be difficult because only abstract information is available, whereas the performance can be improved when the abstracts of references listed in the document can be exploited as augmented information. If each abstract is represented as an instance (i.e., a feature vector) x, then with the augmented information, it can be represented as an instance-bag pair (x;B), where B is a bag of instances (i.e., the abstracts of references). Note that if x has a label y, then we assume that there must exist at least one instance in the bag B having the label y. We regard x and B as two views, i.e., a single-instance view augmented with a multi-instance view, and propose the AMIV-lss approach by establishing a latent semantic subspace between the two views. The AMIV framework can be applied when the augmented information is presented as multi-instance bags and to the best of our knowledge, such a learning with augmented multi-instance view problem has not been touched before. Experimental results on twelve TechPaper datasets, five PubMed data sets and a WebPage data set validate the effectiveness of our AMIV-lss approach.} }
Endnote
%0 Conference Paper %T Learning with Augmented Multi-Instance View %A Yue Zhu %A Jianxin Wu %A Yuan Jiang %A Zhi-Hua Zhou %B Proceedings of the Sixth Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2015 %E Dinh Phung %E Hang Li %F pmlr-v39-zhu14 %I PMLR %P 234--249 %U https://proceedings.mlr.press/v39/zhu14.html %V 39 %X In this paper, we propose the Augmented Multi-Instance View (AMIV) framework to construct a better model by exploiting augmented information. For example, abstract screening tasks may be difficult because only abstract information is available, whereas the performance can be improved when the abstracts of references listed in the document can be exploited as augmented information. If each abstract is represented as an instance (i.e., a feature vector) x, then with the augmented information, it can be represented as an instance-bag pair (x;B), where B is a bag of instances (i.e., the abstracts of references). Note that if x has a label y, then we assume that there must exist at least one instance in the bag B having the label y. We regard x and B as two views, i.e., a single-instance view augmented with a multi-instance view, and propose the AMIV-lss approach by establishing a latent semantic subspace between the two views. The AMIV framework can be applied when the augmented information is presented as multi-instance bags and to the best of our knowledge, such a learning with augmented multi-instance view problem has not been touched before. Experimental results on twelve TechPaper datasets, five PubMed data sets and a WebPage data set validate the effectiveness of our AMIV-lss approach.
RIS
TY - CPAPER TI - Learning with Augmented Multi-Instance View AU - Yue Zhu AU - Jianxin Wu AU - Yuan Jiang AU - Zhi-Hua Zhou BT - Proceedings of the Sixth Asian Conference on Machine Learning DA - 2015/02/16 ED - Dinh Phung ED - Hang Li ID - pmlr-v39-zhu14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 39 SP - 234 EP - 249 L1 - http://proceedings.mlr.press/v39/zhu14.pdf UR - https://proceedings.mlr.press/v39/zhu14.html AB - In this paper, we propose the Augmented Multi-Instance View (AMIV) framework to construct a better model by exploiting augmented information. For example, abstract screening tasks may be difficult because only abstract information is available, whereas the performance can be improved when the abstracts of references listed in the document can be exploited as augmented information. If each abstract is represented as an instance (i.e., a feature vector) x, then with the augmented information, it can be represented as an instance-bag pair (x;B), where B is a bag of instances (i.e., the abstracts of references). Note that if x has a label y, then we assume that there must exist at least one instance in the bag B having the label y. We regard x and B as two views, i.e., a single-instance view augmented with a multi-instance view, and propose the AMIV-lss approach by establishing a latent semantic subspace between the two views. The AMIV framework can be applied when the augmented information is presented as multi-instance bags and to the best of our knowledge, such a learning with augmented multi-instance view problem has not been touched before. Experimental results on twelve TechPaper datasets, five PubMed data sets and a WebPage data set validate the effectiveness of our AMIV-lss approach. ER -
APA
Zhu, Y., Wu, J., Jiang, Y. & Zhou, Z.. (2015). Learning with Augmented Multi-Instance View. Proceedings of the Sixth Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 39:234-249 Available from https://proceedings.mlr.press/v39/zhu14.html.

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