Document Clustering Based On Max-Correntropy Non-Negative Matrix Factorization

Nonnegative matrix factorization (NMF) has been successfully applied to many areas for classification and clustering. Commonly-used NMF algorithms mainly target on minimizing the $l_2$ distance or Kullback-Leibler (KL) divergence, which may not be suitable for nonlinear case. In this paper, we propose a new decomposition method by maximizing the correntropy between the original and the product of two low-rank matrices for document clustering. This method also allows us to learn the new basis vectors of the semantic feature space from the data. To our knowledge, we haven't seen any work has been done by maximizing correntropy in NMF to cluster high dimensional document data. Our experiment results show the supremacy of our proposed method over other variants of NMF algorithm on Reuters21578 and TDT2 databasets.Comment: International Conference of Machine Learning and Cybernetics (ICMLC) 201

Graph Regularized Non-negative Matrix Factorization By Maximizing Correntropy

Non-negative matrix factorization (NMF) has proved effective in many clustering and classification tasks. The classic ways to measure the errors between the original and the reconstructed matrix are $l_2$ distance or Kullback-Leibler (KL) divergence. However, nonlinear cases are not properly handled when we use these error measures. As a consequence, alternative measures based on nonlinear kernels, such as correntropy, are proposed. However, the current correntropy-based NMF only targets on the low-level features without considering the intrinsic geometrical distribution of data. In this paper, we propose a new NMF algorithm that preserves local invariance by adding graph regularization into the process of max-correntropy-based matrix factorization. Meanwhile, each feature can learn corresponding kernel from the data. The experiment results of Caltech101 and Caltech256 show the benefits of such combination against other NMF algorithms for the unsupervised image clustering

Resident Attitudes toward Dark Tourism, a Perspective of Place-based Identity Motives

Place-based identity theories prove to be valid in better understanding resident attitudes towards support for tourism. Yet, its effectiveness is not verified in the context of dark tourism and resident attitudes towards dark tourism remains unknown. Based on a survey of 526 local residents in China’s Yingxiu, the epicentre of the Great Wenchuan Earthquake, the authors examined the relationships between the local residents’ place-based identity motives and their attitudes towards support for dark tourism development. Results show that the motive of ‘belonging/meaning’ is one of the most important determinants; residents’ involvement in dark tourism and bereavement affect their identity motives and attitudes towards support for dark tourism. The theoretical contributions and managerial implications are discussed

A Tale of Two Villages: Debordering and Rebordering in the Bordered Community Scenic Area

Border is part of the entrenched history and reality of tourist mobility. This study takes the concept of border as the theorical basis to analyze how local borders are produced, developed and transformed in tourism communities. Taking China’s Hongcun Village, a bordered UNESCO World Cultural Heritage Site, and its neighboring community Jicun as the study cases, the authors conducted interviews and observation to explore how local borders are developed. The results show that local borders can be understood from five perspectives in Hongcun Scenic Area: administrative, physical, social-economic, functional and psychological. They are not fixed but interacting with each other and constantly changing. This paper contributes to the literature as it reveals that local borders are always driven by external forces and actors, strongly supported by the market economy. And it conceptualizes borders as processes including bordering, debordering and rebordering, which provides a dynamic perspective to understand tourism impacts

The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication

The paper considers a distributed version of deep reinforcement learning (DRL) for multi-agent decision-making process in the paradigm of federated learning. Since the deep neural network models in federated learning are trained locally and aggregated iteratively through a central server, frequent information exchange incurs a large amount of communication overheads. Besides, due to the heterogeneity of agents, Markov state transition trajectories from different agents are usually unsynchronized within the same time interval, which will further influence the convergence bound of the aggregated deep neural network models. Therefore, it is of vital importance to reasonably evaluate the effectiveness of different optimization methods. Accordingly, this paper proposes a utility function to consider the balance between reducing communication overheads and improving convergence performance. Meanwhile, this paper develops two new optimization methods on top of variation-aware periodic averaging methods: 1) the decay-based method which gradually decreases the weight of the model's local gradients within the progress of local updating, and 2) the consensus-based method which introduces the consensus algorithm into federated learning for the exchange of the model's local gradients. This paper also provides novel convergence guarantees for both developed methods and demonstrates their effectiveness and efficiency through theoretical analysis and numerical simulation results