A Neural Network for PCA and Beyond
Neural Processing Letters
Preintegration lateral inhibition enhances unsupervised learning
Neural Computation
Local Non-Negative Matrix Factorization as a Visual Representation
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Non-negative matrix factorization based methods for object recognition
Pattern Recognition Letters
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
A Generalized Divergence Measure for Nonnegative Matrix Factorization
Neural Computation
Learning Image Components for Object Recognition
The Journal of Machine Learning Research
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This paper demonstrates that nonnegative matrix factorisation is mathematically related to a class of neural networks that employ negative feedback as a mechanism of competition. This observation inspires a novel learning algorithm which we call Divisive Input Modulation (DIM). The proposed algorithm provides a mathematically simple and computationally efficient method for the unsupervised learning of image components, even in conditions where these elementary features overlap considerably. To test the proposed algorithm, a novel artificial task is introduced which is similar to the frequently-used bars problem but employs squares rather than bars to increase the degree of overlap between components. Using this task, we investigate how the proposed method performs on the parsing of artificial images composed of overlapping features, given the correct representation of the individual components; and secondly, we investigate how well it can learn the elementary components from artificial training images. We compare the performance of the proposed algorithm with its predecessors including variations on these algorithms that have produced state-of-the-art performance on the bars problem. The proposed algorithm is more successful than its predecessors in dealing with overlap and occlusion in the artificial task that has been used to assess performance.