Additive versus exponentiated gradient updates for linear prediction
We consider two algorithms for on-line prediction based on a linear model. The algorithms are the well-known Gradient Descent (GD) algorithm and a new algorithm, which we call EG *. They both maintain a weight vector using simple updates. For the GD algorithm, the weight vector is updated by subtracting from it the gradient of the squared error made on a prediction multiplied by a parameter called the learning rate. The EG* uses the components of the gradient in the exponents of factors that are used in updating the weight vector multiplicatively. We present worst-case on-line loss bounds for EG* and compare them to previously known bounds for the GD algorithm. The bounds suggest that although the on-line losses of the algorithms are in general incomparable, EG * has a much smaller loss if only few of the input variables are relevant for the predictions. Experiments show that the worst-case upper bounds are quite tight already on simple artificial data. Our main methodological idea is using a distance function between weight vectors both in motivating the algorithms and as a potential function in an amortized analysis that leads to worst-case loss bounds. Using squared Euclidean distance leads to the GD algorithm, and using the relative entropy leads to the EG* algorithm.