This paper presents an unsupervised deep-learning framework named local deep-feature alignment (LDFA) for dimension reduction. We construct neighbourhood for each data sample and learn a local stacked contractive autoencoder (SCAE) from the neighbourhood to extract the local deep features. Next, we exploit an affine transformation to align the local deep features of each neighbourhood with the global features. Moreover, we derive an approach from LDFA to map explicitly a new data sample into the learned low-dimensional subspace. The advantage of the LDFA method is that it learns both local and global characteristics of the data sample set: the local SCAEs capture local characteristics contained in the data set, while the global alignment procedures encode the interdependencies between neighbourhoods into the final lowdimensional feature representations. Experimental results on data visualization, clustering, and classification show that the LDFA method is competitive with several well-known dimension reduction techniques, and exploiting locality in deep learning is a research topic worth further exploring. . His research interests include but are not limited to machine learning, computer animation, and image processing. He serves as a reviewer of several prestigious journals in his research domain. Jun Yu (M'13) received the B.Eng. and Ph.D.
Recently, REF proposed an unsupervised framework called Local Deep-Feature Alignment.
Local Deep-Feature Alignment for Unsupervised Dimension Reduction
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Mathematics Computer Science Medicine