Manifold Learning for Visualizing and Analyzing High-Dimensional Data
IEEE Intelligent Systems
A graph-based topic extraction method enabling simple interactive customization
Proceedings of the 2013 ACM symposium on Document engineering
| Hi-index | 0.00 |
Subspace mapping methods aim at projecting high-dimensional data into a subspace where a specific objective function is optimized. Such dimension reduction allows the removal of collinear and irrelevant variables for creating informative visualizations and task-related data spaces. These specific and generally de-noised subspaces spaces enable machine learning methods to work more efficiently. We present a new and general subspace mapping method, Correlative Matrix Mapping (CMM), and evaluate its abilities for category-driven text organization by assessing neighborhood preservation, class coherence, and classification. This approach is evaluated for the challenging task of processing short and noisy documents.