Regional Income Divergence in China

Numerous policy studies have argued that conditions have prevailed in China since the open door economic reforms of the late 1970s that have encouraged rapid growth at the expense of regional income inequality across the provinces of China. In this paper we use recently developed nonstationary panel techniques to provide empirical support for the fact that the long run tendency since the reforms has been for provincial level incomes to continue to diverge. More importantly, we show that this divergence cannot be attributed to the presence of separate, regional convergence clubs divided among common geographic subgroupings such as the coastal versus interior provinces. Furthermore, we also show that the divergence cannot be attributed to differences in the degree of preferential open-door policies. Rather, we find that the divergence is pervasive both nationally and within these various regional and political subgroupings. We argue that these results point to other causes for regional income divergence, and they also carry potentially important implications for other regions of the world.China, convergence, nonstationary panels

Fast Low-Rank Matrix Learning with Nonconvex Regularization

Low-rank modeling has a lot of important applications in machine learning, computer vision and social network analysis. While the matrix rank is often approximated by the convex nuclear norm, the use of nonconvex low-rank regularizers has demonstrated better recovery performance. However, the resultant optimization problem is much more challenging. A very recent state-of-the-art is based on the proximal gradient algorithm. However, it requires an expensive full SVD in each proximal step. In this paper, we show that for many commonly-used nonconvex low-rank regularizers, a cutoff can be derived to automatically threshold the singular values obtained from the proximal operator. This allows the use of power method to approximate the SVD efficiently. Besides, the proximal operator can be reduced to that of a much smaller matrix projected onto this leading subspace. Convergence, with a rate of O(1/T) where T is the number of iterations, can be guaranteed. Extensive experiments are performed on matrix completion and robust principal component analysis. The proposed method achieves significant speedup over the state-of-the-art. Moreover, the matrix solution obtained is more accurate and has a lower rank than that of the traditional nuclear norm regularizer.Comment: Long version of conference paper appeared ICDM 201

Online Deception Detection Refueled by Real World Data Collection

The lack of large realistic datasets presents a bottleneck in online deception detection studies. In this paper, we apply a data collection method based on social network analysis to quickly identify high-quality deceptive and truthful online reviews from Amazon. The dataset contains more than 10,000 deceptive reviews and is diverse in product domains and reviewers. Using this dataset, we explore effective general features for online deception detection that perform well across domains. We demonstrate that with generalized features - advertising speak and writing complexity scores - deception detection performance can be further improved by adding additional deceptive reviews from assorted domains in training. Finally, reviewer level evaluation gives an interesting insight into different deceptive reviewers' writing styles.Comment: 10 pages, Accepted to Recent Advances in Natural Language Processing (RANLP) 201