This paper analyzes the effectiveness of single-layer networks in unsupervised feature learning. The authors evaluate several feature learning algorithms—sparse auto-encoders, sparse RBMs, K-means clustering, and Gaussian mixtures—on the CIFAR, NORB, and STL datasets using only single-layer networks. They find that the number of hidden nodes and dense feature extraction are critical to achieving high performance. Their results show that using a large number of features and dense feature extraction can lead to state-of-the-art performance on both CIFAR-10 and NORB. Surprisingly, K-means clustering, which is simple and fast, achieves the best performance, outperforming more complex algorithms. The study highlights the importance of network parameters such as receptive field size, number of features, and stride in achieving high performance. The authors conclude that while more complex algorithms may offer greater representational power, simple and fast algorithms can be highly competitive when properly tuned. The results suggest that the choice of network structure is as important as the choice of unsupervised learning algorithm.This paper analyzes the effectiveness of single-layer networks in unsupervised feature learning. The authors evaluate several feature learning algorithms—sparse auto-encoders, sparse RBMs, K-means clustering, and Gaussian mixtures—on the CIFAR, NORB, and STL datasets using only single-layer networks. They find that the number of hidden nodes and dense feature extraction are critical to achieving high performance. Their results show that using a large number of features and dense feature extraction can lead to state-of-the-art performance on both CIFAR-10 and NORB. Surprisingly, K-means clustering, which is simple and fast, achieves the best performance, outperforming more complex algorithms. The study highlights the importance of network parameters such as receptive field size, number of features, and stride in achieving high performance. The authors conclude that while more complex algorithms may offer greater representational power, simple and fast algorithms can be highly competitive when properly tuned. The results suggest that the choice of network structure is as important as the choice of unsupervised learning algorithm.