Image Representation Learning Using Graph Regularized Auto-Encoders
This work addresses image representation challenges for tasks like clustering, offering a domain-specific improvement.
The paper tackles the problem of image representation for unsupervised and semi-supervised learning by proposing Graph regularized Auto-Encoder (GAE), which maps raw image vectors to a compact space that captures latent structures and respects geometric properties, showing encouraging results in image clustering compared to state-of-the-art methods.
We consider the problem of image representation for the tasks of unsupervised learning and semi-supervised learning. In those learning tasks, the raw image vectors may not provide enough representation for their intrinsic structures due to their highly dense feature space. To overcome this problem, the raw image vectors should be mapped to a proper representation space which can capture the latent structure of the original data and represent the data explicitly for further learning tasks such as clustering. Inspired by the recent research works on deep neural network and representation learning, in this paper, we introduce the multiple-layer auto-encoder into image representation, we also apply the locally invariant ideal to our image representation with auto-encoders and propose a novel method, called Graph regularized Auto-Encoder (GAE). GAE can provide a compact representation which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure. Extensive experiments on image clustering show encouraging results of the proposed algorithm in comparison to the state-of-the-art algorithms on real-word cases.