Evaluation of Sub-Selection Methods for Assessing Climate Change Impacts on Low-Flow and Hydrological Drought Conditions

A challenge for climate impact studies is the identification of a sub-set of climate model projections from the many typically available. Sub-selection has potential benefits, including making large datasets more meaningful and uncovering underlying relationships. We examine the ability of seven sub-selection methods to capture low flow and drought characteristics simulated from a large ensemble of climate models for two catchments. Methods include Multi-Cluster Feature Selection (MCFS), Unsupervised Discriminative Features Selection (UDFS), Diversity-Induced Self-Representation (DISR), Laplacian score (L Score), Structure Preserving Unsupervised Feature Selection (SPUFS), Non-convex Regularized Self-Representation (NRSR) and Katsavounidis–Kuo–Zhang (KKZ). We find that sub-selection methods perform differently in capturing varying aspects of the parent ensemble, i.e. median, lower or upper bounds. They also vary in their effectiveness by catchment, flow metric and season, making it very difficult to identify a best sub-selection method for widespread application. Rather, researchers need to carefully judge sub-selection performance based on the aims of their study, the needs of adaptation decision making and flow metrics of interest, on a catchment by catchment basi

Unsupervised feature selection via diversity-induced self-representation

Feature selection is to select a subset of relevant features from the original feature set. In practical applications, regarding the unavailability of an amount of the labels is still a challenging problem. To overcome this problem, unsupervised feature selection algorithms have been developed and achieve promising performance. However, most existing approaches consider only the representativeness of features, but the diversity of features which may lead to the high redundancy and the losses of valuable features are ignored. In this paper, we propose a Diversity-induced Self-representation (DISR) based unsupervised feature selection method to effectively select the features with both representativeness and diversity. Specifically, based on the inherent self-representation property of features, the most representative features can be selected. Meanwhile, to preserve the diversity of selected features and reduce the redundancy of the original features as soon as possible, we introduce a novel diversity term, which adjusts the weights of selected features by incorporating the similarities between features. We then present an efficient algorithm to solve the optimization problem by using the inexact Augmented Lagrange Method (ALM). Finally, both clustering and classification tasks are used to evaluate the proposed method. Empirical results on the synthetic dataset and nine real-world datasets demonstrate the superiority of our method compared with state-of-the-art algorithms