Incomplete Modality Transfer Learning via Latent Low-Rank Constraint

Abstract

When insufficient or incomplete multi-modality data are available in training, the corresponding classi-fication learning may lead to poor training performance or even failure. In order to tackle with this problem, the transfer learning algorithm called IMTL (incomplete modality transfer learning via latent low-rank constraint) is proposed in this paper. The proposed algorithm addresses the incomplete modality problem in two ways. Firstly, latent factors are introduced into a low-rank constrained subspace framework so as to mine missing modality infor-mation on the target domain. With the help of an auxiliary yet complete modality dataset, the proposed cross-modality and cross-dataset transfer learning strategy is used to help align data between modalities or datasets. Sec-ondly, a small amount of labeled target data is used to align the supervision information so as to maintain the internal structure of the target data during the transfer learning. Experimental results show that the proposed algorithm outperforms the previous transfer learning algorithms, and significantly improves the classification performance on the adopted incomplete target datasets.

Keywords

• |transfer learning|incomplete modality|latent low-rank constraint