NDLI: Unsupervised domain adaptation without source domain training samples: a maximum margin clustering based approach

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Unsupervised domain adaptation without source domain training samples: a maximum margin clustering based approach

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Unsupervised domain adaptation without source domain training samples: a maximum margin clustering based approach

Content Provider	ACM Digital Library
Author	Merchant, Shabbir N. Saha, Sudipan Banerjee, Biplab
Abstract	Unsupervised domain adaptation (DA) techniques inherently assume the presence of ample amount of source domain training samples in addition to the target domain test data. The domains are characterized by domain-specific probability distributions governing the data which are substantially different from each other. The goal is to build a task oriented classifier model that performs proportionately in both the domains. In contrary to the standard unsupervised DA setup, we propose a maximum-margin clustering (MMC) based framework for the same which does not consider source domain labeled samples. Instead we formulate it as a joint clustering problem of all the samples from both the domains in a common feature subspace. The Geodesic Flow Kernel (GFK) based subspace projection technique in the Grassmannian manifold is adopted to cast the samples in a domain invariant space. Further, the MMC stage is followed to simultaneously group the data based on the maximization of margins and a classifier is learned for each group. The data overlapping problem is taken care of by specifically learning a SVM-KNN classifier for the potentially unreliable samples per group. We validate the framework on a pair of remote sensing images of different modalities for the purpose of land-cover classification and a generic object dataset for recognition. We observe that the proposed method exhibits performances at par with the fully supervised case for both the tasks but without the requirement of costly annotations.
Starting Page	1
Ending Page	8
Page Count	8
File Format	PDF
ISBN	9781450347532
DOI	10.1145/3009977.3010033
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-12-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Domain adaptation Geodesic flow kernel Max-margin clustering
Content Type	Text
Resource Type	Article

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