A better way to learn features

A TYPICAL MACHINE learning program uses weighted combinations of features to discriminate between classes or to predict real-valued outcomes. The art of machine learning is in constructing the features, and a radically new method of creating features constitutes a major advance. In the 1980s, the new method was backpropagation, which uses the chain rule to backpropagate error derivatives through a multilayer, feed-forward, neural network and adjusts the weights between layers by following the gradient of the backpropagated error. This worked well for recognizing simple shapes, such as handwritten digits, especially in convolutional neural networks that use local feature detectors replicated across the image. 5 For many tasks, however, it proved extremely difficult to optimize deep neural nets with many layers of non-linear features, and a huge number of labeled training cases was required for large neural networks to generalize well to test data. In the 1990s, Support Vector Machines (SVMs) 8 introduced a very different way of creating features: the user defines a kernel function that computes the similarity between two input vectors, then a judiciously chosen subset of the training examples is used to create " landmark " features that measure how similar a test case is to each training case. SVMs have a clever way of choosing which training cases to use as landmarks and deciding how to weight them. They work remarkably well on many machine learning tasks even though the selected features are non-adaptive. The success of SVMs dampened the earlier enthusiasm for neural networks. More recently, however, it has been shown that multiple layers of feature detectors can be learned greedily, one layer at a time, by using unsupervised learning that does not require labeled data. The features in each layer are designed to model the statistical structure of the patterns of feature activations in the previous layer. After learning several layers of features this way without paying any attention to the final goal, many of the high-level features will be irrelevant for any particular task, but others will be highly relevant because high-order correlations are the signature of the data's true underlying causes and the labels are more directly related to these causes than to the raw inputs. A subsequent stage of fine-tuning using backpropagation then yields neural networks that work much better than those trained by backpropagation alone and better than SVMs for important tasks such as object or speech recognition. The neural …