Domain Adaptation with Nonparametric Projections

Date

2019-08-22

Author

Vural, Elif

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Domain adaptation algorithms focus on a setting where the training and test data are sampled from related but different distributions. Various domain adaptation methods aim to align the source and target domains in a new common domain by learning a transformation or projection. In this work, we learn a nonlinear and nonparametric projection of the source and target domains into a common domain along with a linear classifier in the new domain. Experiments on image data sets show that the proposed nonlinear approach outperforms baseline domain adaptation methods based on linear transformations.

Subject Keywords

Domain adaptation, Manifold learning, Nonlinear transformations, Classification

URI

https://hdl.handle.net/11511/40667

DOI

https://doi.org/10.1109/siu.2019.8806543

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Department of Electrical and Electronics Engineering, Conference / Seminar

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DOMAIN ADAPTATION VIA TRANSFERRING SPECTRAL PROPERTIES OF LABEL FUNCTIONS ON GRAPHS Pilanci, Mehmet; Vural, Elif (2016-07-12) We propose a domain adaptation algorithm that relies on a graph representation of data samples in the source and target domains. The algorithm combines the information of the known class labels in the source and target domains through the Fourier coefficients of the class label function in the two graphs. The proposed method does not require an ordering or a one-to-one mapping between the samples of the source and target domains; instead, it uses only a small set of matched pairs that serve the purpose of "...
Domain Adaptation on Graphs via Frequency Analysis Pilancı, Mehmet; Vural, Elif (2019-08-22) Classical machine learning algorithms assume the training and test data to be sampled from the same distribution, while this assumption may be violated in practice. Domain adaptation methods aim to exploit the information available in a source domain in order to improve the performance of classification in a target domain. In this work, we focus on the problem of domain adaptation in graph settings. We consider a source graph with many labeled nodes and aim to estimate the class labels on a target graph wit...
GENERALIZATION BOUNDS FOR DOMAIN ADAPTATION VIA DOMAIN TRANSFORMATIONS Vural, Elif (2018-09-20) Many domain adaptation methods are based on learning a projection or a transformation of the source and target domains to a common domain and training a classifier there, while the performance of such algorithms has not been theoretically studied yet. Previous studies proposing generalization bounds for domain adaptation relate the target loss to the discrepancy between the source and target distributions, however, do not take into account the possible effects of learning a transformation between the two do...
Learning Smooth Pattern Transformation Manifolds Vural, Elif (2013-04-01) Manifold models provide low-dimensional representations that are useful for processing and analyzing data in a transformation-invariant way. In this paper, we study the problem of learning smooth pattern transformation manifolds from image sets that represent observations of geometrically transformed signals. To construct a manifold, we build a representative pattern whose transformations accurately fit various input images. We examine two objectives of the manifold-building problem, namely, approximation a...
Distance-based discretization of parametric signal manifolds Vural, Elif (2010-06-28) The characterization of signals and images in manifolds often lead to efficient dimensionality reduction algorithms based on manifold distance computation for analysis or classification tasks. We propose in this paper a method for the discretization of signal manifolds given in a parametric form. We present an iterative algorithm for the selection of samples on the manifold that permits to minimize the average error in the manifold distance computation. Experimental results with image appearance manifolds d...

Citation Formats

E. Vural, “Domain Adaptation with Nonparametric Projections,” 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40667.