AutoEncoder Inspired Unsupervised Feature Selection

High-dimensional data in many areas such as computer vision and machine learning tasks brings in computational and analytical difficulty. Feature selection which selects a subset from observed features is a widely used approach for improving performance and effectiveness of machine learning models with high-dimensional data. In this paper, we propose a novel AutoEncoder Feature Selector (AEFS) for unsupervised feature selection which combines autoencoder regression and group lasso tasks. Compared to traditional feature selection methods, AEFS can select the most important features by excavating both linear and nonlinear information among features, which is more flexible than the conventional self-representation method for unsupervised feature selection with only linear assumptions. Experimental results on benchmark dataset show that the proposed method is superior to the state-of-the-art method.Comment: accepted by ICASSP 201

Rising regional income inequality in China: Fact or artefact?

China’s local populations can be counted in two ways; by how many people have hukou household registration from each place and by how many people actually reside in each place. The counts differ by the non-hukou migrants – people that move from their place of registration – who have grown from fewer than five million when reform began in 1978 to over 200 million by 2010. For most of the first three decades of the reform era, the hukou count was used to produce per capita GDP figures. In coastal provinces the resident count is many millions more than the hukou count, while for migrant-sending provinces it is the reverse, creating a systematic and time-varying distortion in provincial GDP per capita. Moreover, a sharp discontinuity occurred when provinces recently switched from the hukou count to the resident count when reporting GDP per capita. A double-count also resulted because some provinces switched before others and initial resident counts were incomplete. This paper describes the changing definition of provincial populations in China and their impact on inequality in provincial GDP per capita. We show that much of the apparent increase in inter-provincial inequality disappears once a consistent series of GDP per resident is used