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Learning from Multiple Outlooks. Maayan Harel and Shie Mannor ICML 2011 Presented by Minhua Chen. What You Saw is Not What You Get: Domain Adaptation Using Asymmetric Kernel Transforms CVPR2011. Introduction.

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learning from multiple outlooks

Learning from Multiple Outlooks

MaayanHarel and ShieMannor

ICML 2011

Presented by Minhua Chen

  • A learning task often relates to multiple representations, or called domains, outlooks.
  • For example, in activity recognition, each user (outlook) may use different sensors.
  • There are no sample correspondence, nor feature correspondence across outlooks; only the label space (classification task) is shared.
  • The goal is to use the information in all outlooks to improve learning performance.
  • The approach is to map the data in one outlook (source) to another (target), so that the effective sample size is enlarged in the target domain.
problem formulation
Problem Formulation
  • The central question is how to map data from one domain to the other, possibly with different feature dimensions.
  • The authors proposed an algorithm that computes optimal affine mapping by matching moments of the empirical distribution for each class.

Source domain

Target domain

mathematical solution
Mathematical Solution
  • Procrustes analysis can be applied to solve Ri.
  • The formulation can be extended to multiple outlooks:
  • Activity recognition task with the following human activities: walking, running, going upstairs, going downstairs, lingering.
  • Data recorded by different users are regarded as different outlooks (domains), since the sensors used are different.
  • Two setups are examined: domain adaptation with shared feature space, and multiple outlooks with different feature spaces.
  • The authors tested the success of the mapping algorithm by classification of the target test data with a SVM classifier trained on the mapped source data.
what you saw is not what you get domain adaptation using asymmetric kernel transforms

What You Saw is Not What You Get: Domain Adaptation Using Asymmetric Kernel Transforms

B. Kulis, K. Saenko and T. Darrel, CVPR 2011