Blind Domain Adaptation With Augmented Extreme Learning Machine Features

IEEE Transactions on Cybernetics
Muhammad Uzair, Ajmal Mian

Abstract

In practical applications, the test data often have different distribution from the training data leading to suboptimal visual classification performance. Domain adaptation (DA) addresses this problem by designing classifiers that are robust to mismatched distributions. Existing DA algorithms use the unlabeled test data from target domain during training time in addition to the source domain data. However, target domain data may not always be available for training. We propose a blind DA algorithm that does not require target domain samples for training. For this purpose, we learn a global nonlinear extreme learning machine (ELM) model from the source domain data in an unsupervised fashion. The global ELM model is then used to initialize and learn class specific ELM models from the source domain data. During testing, the target domain features are augmented with the reconstructed features from the global ELM model. The resulting enriched features are then classified using the class specific ELM models based on minimum reconstruction error. Extensive experiments on 16 standard datasets show that despite blind learning, our method outperforms six existing state-of-the-art methods in cross domain visual recognition.

References

Dec 27, 2008·IEEE Transactions on Pattern Analysis and Machine Intelligence·John WrightYi Ma
Mar 20, 2010·IEEE Transactions on Pattern Analysis and Machine Intelligence·Lorenzo Bruzzone, Mattia Marconcini
Nov 26, 2010·IEEE Transactions on Neural Networks·Sinno Jialin PanQiang Yang
Mar 1, 2012·IEEE Transactions on Neural Networks and Learning Systems·Lixin DuanIvor Wai-Hung Tsang
Jul 30, 2014·IEEE Transactions on Neural Networks and Learning Systems·Xia LiuZongben Xu
Sep 16, 2014·IEEE Transactions on Cybernetics·Zuo BaiM Brandon Westover
Nov 22, 2014·IEEE Transactions on Cybernetics·Zhen CuiXuelong Li
Nov 22, 2014·IEEE Transactions on Cybernetics·Gao HuangCheng Wu

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Citations

May 28, 2019·Sensors·Ahmed Salih Al-KhaleefaReza Firsandaya Malik

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