Adaptive Lifting Scheme Based on Orthogonal Polynomials
To determine the prediction and updated parameters of lifting scheme,this paper takes the sum of squared detail components as an objective function,and designs predictors based on orthogonal polynomial,so that the prediction parameter can represent the features of the given data.An optimal predictor and an optimal updater are chosen for the transforming sample according to the magnitude of the correlation factors,so the lifting scheme can fit the change of original signal better.The theory and simulation results demonstrate that the filtering performance of adaptive lifting scheme based on orthogonal polynomials is better than that based on general fitting method and traditional wavelet db5,especially with low signal-to-noise ratio.
Diversified retrieval and online learning are two core research areas in the design of modern information retrieval systems. In this paper, we propose the linear sub-modular bandits problem, which is an online learning setting for optimizing a general class of feature-rich submodular utility models for diversified retrieval. We present an algorithm, called LSBGREEDY, and prove that it efficiently converges to a near-optimal model. As a case study, we applied our approach to the setting of personalized news recommendation, where the system must recommend small sets of news articles selected from tens of thousands of available articles each day. In a live user study, we found that LSBGREEDY significantly outperforms existing online learning approaches.