NDLI: Dictionary based sparse representation for domain adaptation

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Dictionary based sparse representation for domain adaptation

Content Provider	ACM Digital Library
Author	Mehrotra, Rishabh Agrawal, Rushabh Haider, Syed Aqueel
Abstract	Machine Learning algorithms are often as good as the data they can learn from. Enormous amount of unlabeled data is readily available and the ability to efficiently use such amount of unlabeled data holds a significant promise in terms of increasing the performance of various learning tasks. We consider the task of supervised Domain Adaptation and present a Self-Taught learning based framework which makes use of the K-SVD algorithm for learning sparse representation of data in an unsupervised manner. To the best of our knowledge this is the first work that integrates K-SVD algorithm into the self-taught learning framework. The K-SVD algorithm iteratively alternates between sparse coding of the instances based on the current dictionary and a process of updating/adapting the dictionary to better fit the data so as to achieve a sparse representation under strict sparsity constraints. Using the learnt dictionary, a rich feature representation of the few labeled instances is obtained which is fed to a classifier along with class labels to build the model. We evaluate our framework on the task of domain adaptation for sentiment classification. Both self-domain (requiring very few domain-specific training instances) and cross-domain classification (requiring 0 labeled instances of target domain and very few labeled instances of source domain) are performed. Empirical comparisons of self-domain and cross-domain results establish the efficacy of the proposed framework.
Starting Page	2395
Ending Page	2398
Page Count	4
File Format	PDF
ISBN	9781450311564
DOI	10.1145/2396761.2398649
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-10-29
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Transfer learning Domain adaptation Sparse representation
Content Type	Text
Resource Type	Article

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