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Paper Note --- Transfer Learning via Dimensionality Reduction

分类: 文章 • 2023-11-05 08:06:45

Paper Background

Proceedings of the Twenty-Third AAAI COnference on Artificial Intelligence 2008
Author:

  1. Sinno Jialin Pan
  2. James T.Kwork
  3. Qiang Yang

Hong Kong University of Science and Technology

This paper propose a new dimensionality reduction method to find a latent feature space, which minimize the distance between distribution of data in source domain and target domain in a latent space, thus we can use standard algorithms to train models.
Paper Note --- Transfer Learning via Dimensionality Reduction
Paper Note --- Transfer Learning via Dimensionality Reduction
Paper Note --- Transfer Learning via Dimensionality Reduction
Paper Note --- Transfer Learning via Dimensionality Reduction
From this formula we learned this kernel matrix K instead of learning the universal kernel k. However we need to ensure that learned kernel matrix does correspond to an universal kernel.
Paper Note --- Transfer Learning via Dimensionality Reduction
about kernel:
from Zhihu
Paper Note --- Transfer Learning via Dimensionality Reduction
Proved that learned kernel matix K is universal.
Paper Note --- Transfer Learning via Dimensionality Reduction
MVU 最大差异展开
Paper Note --- Transfer Learning via Dimensionality Reduction
Paper Note --- Transfer Learning via Dimensionality Reduction

Inspiration

For comparing two different distribution, we map them into a latent space by using kernel matrix, and compare the difference in such sapce will be more resonable.

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