K-means recovers ICA filters when independent components are sparse


Alon Vinnikov, Shai Shalev-Shwartz ;
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):712-720, 2014.


Unsupervised feature learning is the task of using unlabeled examples for building a representation of objects as vectors. This task has been extensively studied in recent years, mainly in the context of unsupervised pre-training of neural networks. Recently, (Coates et al., 2011) conducted extensive experiments, comparing the accuracy of a linear classifier that has been trained using features learnt by several unsupervised feature learning methods. Surprisingly, the best performing method was the simplest feature learning approach that was based on applying the K-means clustering algorithm after a whitening of the data. The goal of this work is to shed light on the success of K-means with whitening for the task of unsupervised feature learning. Our main result is a close connection between K-means and ICA (Independent Component Analysis). Specifically, we show that K-means and similar clustering algorithms can be used to recover the ICA mixing matrix or its inverse, the ICA filters. It is well known that the independent components found by ICA form useful features for classification (Le et al., 2012; 2011; 2010), hence the connection between K-mean and ICA explains the empirical success of K-means as a feature learner. Moreover, our analysis underscores the significance of the whitening operation, as was also observed in the experiments reported in (Coates et al., 2011). Finally, our analysis leads to a better initialization of K-means for the task of feature learning.

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