Minimax center to extract a common subspace from multiple datasets

Renard, Emilie;Absil, Pierre-Antoine;Gallivan, Kyle A.
(2019) 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning(ESANN 2019) — Location: Bruges (24.April.2019)

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Authors
  • Renard, EmilieUCLouvain
    Author
  • Absil, Pierre-AntoineUCLouvain
    Author
  • Gallivan, Kyle A.Florida State University - Department of Mathematics - Tallahassee FL 32306-4510, USA
    Author
Abstract
We address the problem of extracting common information from multiple datasets. More specifically, we look for a common subspace minimizing the maximal dissimilarity with all datasets and we propose an algorithm derived from the first order necessary conditions of optimality. On synthetic datasets the proposed method gives as good results as a Riemannian based approach, but also provides an evaluation on how far the iterate is from a critical point.
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Renard, E., Absil, P.-A., & Gallivan, K. A. (2019). Minimax center to extract a common subspace from multiple datasets. ESANN 2019 Proceedings, p. 275-280. https://hdl.handle.net/2078.5/253900