Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
ICDCS '06 Proceedings of the 26th IEEE International Conference on Distributed Computing Systems
Computers and Operations Research
Applying biclustering to text mining: an immune-inspired approach
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
Graph Regularized Nonnegative Matrix Factorization for Data Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Parameter-Free Framework for General Supervised Subspace Learning
IEEE Transactions on Information Forensics and Security
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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Financial models draw on the need to turn critical (economical) information into better decision making models. When it comes to performance enhancement many advanced techniques have been used in bankruptcy detection with good results, yet rarely biclustering has been considered. In this paper, we propose a two-step approach based first on biclustering and second on subspace learning with constant regularization. The rationale behind biclustering is to discover patterns upholding instances and features that are highly correlated. Moreover, we placed great emphasis on building a weight affinity graph matrix and performing smooth subspace learning with regularization. In particular, the geometric topology of biclusters is preserved during learning. Experimental results demonstrate the success of the approach yielding excellent results in a real French data set of healthy and distressed companies.