In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[Q]: Spectral methods are popular in detecting global structures in the given data that can be represented as a matrix. However when the data matrix is sparse or noisy, classic spectral methods usually fail to work, due to localization of eigenvectors (or singular vectors) induced by the sparsity or noise. In this work, we propose a general method to solve the localization problem by learning a regularization matrix from the localized eigenvectors. Using matrix perturbation analysis, we demonstrate that the learned regularizations suppress down the eigenvalues associated with localized eigenvectors and enable us to recover the informative eigenvectors representing the global structure. We show applications of our method in several inference problems: community detection in networks, clustering from pairwise similarities, rank estimation and matrix completion problems. Using extensive experiments, we illustrate that our method solves the localization problem and works down to the theoretical detectability limits in different kinds of synthetic data. This is in contrast with existing spectral algorithms based on data matrix, non-backtracking matrix, Laplacians and those with rank-one regularizations, which perform poorly in the sparse case with noise.
[A]: Robust Spectral Detection of Global Structures in the Data by Learning a Regularization


[Q]: This paper describes a new method for reducing the error in a classifier. It uses a weight adjustment update, but includes the very simple rule of either adding or subtracting the adjustment, based on whether the data point is currently larger or smaller than the desired value, and on a pointby-point basis. This gives added flexibility to the convergence procedure, where through a series of transpositions, values far away can continue towards the desired value, whereas values that are originally much closer can oscillate from one side to the other. Tests show that the method can successfully classify some known datasets. It can also work in a batch mode, with reduced training times and can be used as part of a neural network, or classifiers in general. There are also some updates on an earlier wave shape paper.
[A]: A New Oscillating-Error Technique for Classifiers


[Q]: To mitigate the uncertainty of variable renewable<lb>resources, two off-the-shelf machine learning tools are deployed<lb>to forecast the solar power output of a solar photovoltaic system.<lb>The support vector machines generate the forecasts and the<lb>random forest acts as an ensemble learning method to combine<lb>the forecasts. The common ensemble technique in wind and solar<lb>power forecasting is the blending of meteorological data from<lb>several sources. In this study though, the present and the past<lb>solar power forecasts from several models, as well as the<lb>associated meteorological data, are incorporated into the random<lb>forest to combine and improve the accuracy of the day-ahead<lb>solar power forecasts. The performance of the combined model is<lb>evaluated over the entire year and compared with other<lb>combining techniques. Keywords—Ensemble learning, post-processing, random forest,<lb>solar power, support vector regression.
[A]:
Random Forest Ensemble of Support Vector Regression Models for Solar Power Forecasting