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Sanatan Sukhija

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3 papers

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AIJ Journal 2019 Journal Article

Supervised heterogeneous feature transfer via random forests

Sanatan Sukhija
Narayanan C. Krishnan

Transfer learning across heterogeneous feature spaces can, in general, be a very difficult problem in practice due to the heterogeneity of features and lack of correspondence between data points of different domains. In this paper, we present a novel supervised domain adaptation algorithm (SHDA-RF) that transfers knowledge from a data-rich source domain to a target domain with only few training instances. The proposed method makes use of random forests to identify pivot features that bridge the two domains. The key idea of the proposed feature transfer approach is that every path in a decision tree leading to a partition of the data is associated with a certain label distribution and the label distributions that appear both in the source and target random forest models can be used as pivots for bridging the two domains. This information is used to generate a sparse feature transformation matrix, which maps patterns from the source feature space to the target feature space. The target model is then retrained along with the projected source data. We conduct extensive experiments on diverse datasets of varying dimensions and sparsity to verify the superiority of the proposed approach over other baseline and state of the art transfer approaches.

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AAAI Conference 2018 Short Paper

Label Space Driven Heterogeneous Transfer Learning With Web Induced Alignment

Sanatan Sukhija

Heterogeneous Transfer Learning (HTL) algorithms leverage knowledge from a heterogeneous source domain to perform a task in a target domain. We present a novel HTL algorithm that works even where there are no shared features, instance correspondences and further, the two domains do not have identical labels. We utilize the label relationships via web-distance to align the data of the domains in the projected space, while preserving the structure of the original data.

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IJCAI Conference 2016 Conference Paper

Supervised Heterogeneous Domain Adaptation via Random Forests

Sanatan Sukhija
Narayanan C Krishnan
Gurkanwal Singh

Heterogeneity of features and lack of correspondence between data points of different domains are the two primary challenges while performing feature transfer. In this paper, we present a novel supervised domain adaptation algorithm (SHDA-RF) that learns the mapping between heterogeneous features of different dimensions. Our algorithm uses the shared label distributions present across the domains as pivots for learning a sparse feature transformation. The shared label distributions and the relationship between the feature spaces and the label distributions are estimated in a supervised manner using random forests. We conduct extensive experiments on three diverse datasets of varying dimensions and sparsity to verify the superiority of the proposed approach over other baseline and state of the art transfer approaches.

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