Convolutional Clustering for Unsupervised Learning
This addresses the tedious and costly need for large labeled datasets in deep learning, though it appears incremental as it builds on existing clustering techniques.
The paper tackles the problem of reducing reliance on labeled data for deep neural networks by proposing an unsupervised learning method based on an enhanced k-means clustering algorithm, achieving a test accuracy of 74.1% on STL-10 and a test error of 0.5% on MNIST.
The task of labeling data for training deep neural networks is daunting and tedious, requiring millions of labels to achieve the current state-of-the-art results. Such reliance on large amounts of labeled data can be relaxed by exploiting hierarchical features via unsupervised learning techniques. In this work, we propose to train a deep convolutional network based on an enhanced version of the k-means clustering algorithm, which reduces the number of correlated parameters in the form of similar filters, and thus increases test categorization accuracy. We call our algorithm convolutional k-means clustering. We further show that learning the connection between the layers of a deep convolutional neural network improves its ability to be trained on a smaller amount of labeled data. Our experiments show that the proposed algorithm outperforms other techniques that learn filters unsupervised. Specifically, we obtained a test accuracy of 74.1% on STL-10 and a test error of 0.5% on MNIST.