2019 • Conference Paper
Minimax center to extract a common subspace from multiple datasets
Authors:
Renard, Emilie,
Absil, Pierre-Antoine ,
Gallivan, Kyle A.
Published in:
27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning(ESANN 2019)
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.