Structure Learning with Similarity Preserving
This work addresses the need for more effective structure learning in data analysis, particularly for tasks sensitive to sample similarity, though it appears incremental as it builds on existing low-rank and sparse modeling approaches.
The paper tackled the problem of extracting hidden manifold structures beyond low-rank or sparse models by proposing a structure learning framework that preserves pairwise similarities between data points, and experiments on benchmark datasets showed consistent and significant performance improvements in clustering and semi-supervised classification tasks.
Leveraging on the underlying low-dimensional structure of data, low-rank and sparse modeling approaches have achieved great success in a wide range of applications. However, in many applications the data can display structures beyond simply being low-rank or sparse. Fully extracting and exploiting hidden structure information in the data is always desirable and favorable. To reveal more underlying effective manifold structure, in this paper, we explicitly model the data relation. Specifically, we propose a structure learning framework that retains the pairwise similarities between the data points. Rather than just trying to reconstruct the original data based on self-expression, we also manage to reconstruct the kernel matrix, which functions as similarity preserving. Consequently, this technique is particularly suitable for the class of learning problems that are sensitive to sample similarity, e.g., clustering and semisupervised classification. To take advantage of representation power of deep neural network, a deep auto-encoder architecture is further designed to implement our model. Extensive experiments on benchmark data sets demonstrate that our proposed framework can consistently and significantly improve performance on both evaluation tasks. We conclude that the quality of structure learning can be enhanced if similarity information is incorporated.