Abstract
This paper introduces a novel classification algorithm for heterogeneous domain adaptation. The algorithm projects both the target and source data into a common feature space of the class decomposition scheme used. The distinctive features of the algorithm are: (1) it does not impose any assumptions on the data other than sharing the same class labels; (2) it allows adaptation of multiple source domains at once; and (3) it can help improving the topology of the projected data for class separability. The algorithm provides two built-in classification rules and allows applying any other classification model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
This is indicated with postfix T.
References
Dekel, O., Singer, Y.: Multiclass learning by probabilistic embeddings. Adv. Neural Inf. Process. Syst. 15, 945–952 (2002)
Dietterich, T., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. J. Artif. Intell. Res. 2, 263–286 (1995)
Duan, L., Xu, D., Tsang, I.: Learning with augmented features for HDA. In: Proceedings of the 29th Internatiobal Conference on Machine Learning (ICML 2012) (2012)
Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley, New York (2000)
Ismailoglu, F., Smirnov, E., Nikolaev, N., Peeters, R.: Instance-based decompositions of error correcting output codes. In: Schwenker, F., Roli, F., Kittler, J. (eds.) MCS 2015. LNCS, vol. 9132, pp. 51–63. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20248-8_5
Zhou, J., Tsang, I., Pan, S., Tan, M.: Heterogeneous domain adaptation for multiple classes. In: Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS 2014), pp. 1095–1103 (2014)
Kulis, B., Saenko, K., Darrell, T.: What you saw is not what you get: domain adaptation using asymmetric kernel transforms. In: Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011), pp. 1785–1792. IEEE (2011)
Lichman, M.: UCI machine learning repository (2013)
Rasiwasia, N., Pereira, J., Coviello, E., Doyle, G., Lanckriet, G., Levy, R., Vasconcelos, N.: A new approach to cross-modal multimedia retrieval. In: Proceedings of the 18th International Conference on Multimedia (MM 2010), pp. 251–260. ACM (2010)
Rifkin, R., Klautau, A.: In defense of one-vs-all classification. J. Mach. Learn. Res. 5, 101–141 (2004)
Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 213–226. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_16
Wang, C., Mahadevan, S.: Heterogeneous domain adaptation using manifold alignment. In: Proceedings of the 22th International Joint Conference on Artificial Intelligence (IJCAI 2011), pp. 1541–1546. AAAI Press (2011)
Weiss, K., Khoshgoftaar, T., Wang, D.D.: A survey of transfer learning. J. Big Data 3(9), 45–85 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Ismailoglu, F., Smirnov, E., Peeters, R., Zhou, S., Collins, P. (2018). Heterogeneous Domain Adaptation Based on Class Decomposition Schemes. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10937. Springer, Cham. https://doi.org/10.1007/978-3-319-93034-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-93034-3_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93033-6
Online ISBN: 978-3-319-93034-3
eBook Packages: Computer ScienceComputer Science (R0)