As a classical subspace learning method, Probabilistic PCA (PPCA) has been extended to several bilinear variants for dealing with matrix observations. However, they are all based on the Tucker model, leading to a restricted subspace representation and the problem of rotational ambiguity. To address these problems, this paper proposes a bilinear PPCA method named as Probabilistic Rank-One Matrix Analysis (PROMA). PROMA is based on the CP model, which leads to a more flexible subspace representation and does not suffer from rotational ambiguity. For better generalization, concurrent regularization is introduced to regularize the whole matrix subspace, rather than column and row factors separately. Experiments on both synthetic and real-world data demonstrate the superiority of PROMA in subspace estimation and classification as well as the effectiveness of concurrent regularization in regularizing bilinear PPCAs.
|Number of pages
|IJCAI International Joint Conference on Artificial Intelligence
|Published - 2016
|25th International Joint Conference on Artificial Intelligence, IJCAI 2016 - New York, United States, New York, United States
Duration: 9 Jul 2016 → 15 Jul 2016
Scopus Subject Areas
- Artificial Intelligence