Initialization enhancer for non-negative matrix factorization
Engineering Applications of Artificial Intelligence
Fast nonnegative matrix factorization and its application for protein fold recognition
EURASIP Journal on Applied Signal Processing
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Nonnegative Lagrangian relaxation of k-means and spectral clustering
ECML'05 Proceedings of the 16th European conference on Machine Learning
The condensed nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
Non-negative Matrix Factorization for Endoscopic Video Summarization
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
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We propose a data reduction method based on fuzzy clustering and nonnegative matrix factorisation. In contrast to different variants of data set editing typically used for data reduction, our method is completely unsupervised, i.e., it does not need class labels to eliminate examples from a data set. Thus, it is useful in exploratory data analysis when class labels of examples are unknown or unavailable in order to gain insight into structure of different groups of patterns. Also unlike many types of unsupervised clustering relating a single example (cluster centroid) to each cluster, our method associates a set of the most representative examples with each cluster. Hence, it makes cluster structure more transparent to a data analyst.