The performance of spectral clustering heavily relies on the quality of affinity matrix. A variety of affinity-matrix-construction (AMC) methods have been proposed but they have hyperparameters to determine beforehand, which requires strong experience and leads to difficulty in real applications, especially when the inter-cluster similarity is high and/or the dataset is large. In addition, we often need to choose different AMC methods for different datasets, which still depends on experience. To solve these two challenging problems, in this paper, we present a simple yet effective method for automated spectral clustering. First, we propose to find the most reliable affinity matrix via grid search or Bayesian optimization among a set of candidates given by different AMC methods with different hyperparameters, where the reliability is quantified by the \textit{relative-eigen-gap} of graph Laplacian introduced in this paper. Second, we propose a fast and accurate AMC method based on least squares representation and thresholding and prove its effectiveness theoretically. Finally, we provide a large-scale extension for the automated spectral clustering method, of which the time complexity is linear with the number of data points. Extensive experiments of natural image clustering show that our method is more versatile, accurate, and efficient than baseline methods.

This paper is aimed to predict pain perception from laser-evoked EEG oscillatory activities in the time-frequency domain with multivariate pattern analysis (MVPA). We first identify pre-/post-stimulus EEG oscillatory activities that are correlated with the intensity of laser-evoked pain perception using a multivariate linear regression (MVLR) model, which is solved by partial least-squares regression (PLSR). Further, we used the MVLR model to predict the intensity of pain perception from identified pain-correlated time-frequency EEG data for each subject. Our results showed that the proposed MVLR prediction model provided a qualitative prediction of pain (classification of low pain and high pain) with an accuracy of 78.53 ± 1.16% and a quantitative prediction of pain (on a continuous scale from 0 to 10) with a mean absolute error (MAE) of 1.45 ± 0.05, both of which are significantly better than the results of the conventional pain prediction based on single-trial detection of laser-evoked potentials. Besides, for the first time it was found that the pre-stimulus EEG oscillation could significantly contribute to the prediction, which extended our notion of the determinants of pain perception.