Local Deep-Feature Alignment for Unsupervised Dimension Reduction
This addresses the problem of extracting meaningful low-dimensional representations from unlabeled data for researchers in machine learning, but it is incremental as it builds on existing deep learning and locality-based methods.
The paper tackles unsupervised dimension reduction by proposing Local Deep-Feature Alignment (LDFA), a framework that learns local deep features via Stacked Contractive Auto-encoders and aligns them globally, resulting in competitive performance in visualization, clustering, and classification compared to existing techniques.
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 Auto-encoder (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 low-dimensional 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.