Effective Discriminative Feature Selection with Non-trivial Solutions

Feature selection and feature transformation, the two main ways to reduce dimensionality, are often presented separately. In this paper, a feature selection method is proposed by combining the popular transformation based dimensionality reduction method Linear Discriminant Analysis (LDA) and sparsity regularization. We impose row sparsity on the transformation matrix of LDA through ${\ell}_{2,1}$-norm regularization to achieve feature selection, and the resultant formulation optimizes for selecting the most discriminative features and removing the redundant ones simultaneously. The formulation is extended to the ${\ell}_{2,p}$-norm regularized case: which is more likely to offer better sparsity when $0<p<1$. Thus the formulation is a better approximation to the feature selection problem. An efficient algorithm is developed to solve the ${\ell}_{2,p}$-norm based optimization problem and it is proved that the algorithm converges when $0<p\le 2$. Systematical experiments are conducted to understand the work of the proposed method. Promising experimental results on various types of real-world data sets demonstrate the effectiveness of our algorithm

A Convex Formulation for Spectral Shrunk Clustering

Spectral clustering is a fundamental technique in the field of data mining and information processing. Most existing spectral clustering algorithms integrate dimensionality reduction into the clustering process assisted by manifold learning in the original space. However, the manifold in reduced-dimensional subspace is likely to exhibit altered properties in contrast with the original space. Thus, applying manifold information obtained from the original space to the clustering process in a low-dimensional subspace is prone to inferior performance. Aiming to address this issue, we propose a novel convex algorithm that mines the manifold structure in the low-dimensional subspace. In addition, our unified learning process makes the manifold learning particularly tailored for the clustering. Compared with other related methods, the proposed algorithm results in more structured clustering result. To validate the efficacy of the proposed algorithm, we perform extensive experiments on several benchmark datasets in comparison with some state-of-the-art clustering approaches. The experimental results demonstrate that the proposed algorithm has quite promising clustering performance.Comment: AAAI201

Observation of Landau level-like quantizations at 77 K along a strained-induced graphene ridge

Recent studies show that the electronic structures of graphene can be modified by strain and it was predicted that strain in graphene can induce peaks in the local density of states (LDOS) mimicking Landau levels (LLs) generated in the presence of a large magnetic field. Here we report scanning tunnelling spectroscopy (STS) observation of nine strain-induced peaks in LDOS at 77 K along a graphene ridge created when the graphene layer was cleaved from a sample of highly oriented pyrolytic graphite (HOPG). The energies of these peaks follow the progression of LLs of massless 'Dirac fermions' (DFs) in a magnetic field of 230 T. The results presented here suggest a possible route to realize zero-field quantum Hall-like effects at 77 K

The Integrated Platform of Digital Cultural Heritage in China: a Proposed Model Based on Public’s Expectations

This poster attempts to propose an integrated platform of digital cultural heritage in China based on the public’s expectations and provide specific suggestions for policy makers. A questionnaire was designed and disseminated through online survey service website. From 6 October to November 2016, a total of 1,076 responses were collected. The data showed that the Chinese users expected a comprehensive, convenient, and unified one-stop online accessible portal to all types of digital cultural heritage from China. Based on user need analysis, an integrated platform model of digital cultural heritage has been proposed. Also the China’s digital cultural heritage integration management system has been proposed. In this system, the corporation between the Ministry of Culture and the State Archives Administration of China can be realized

Coupled thermal-structural modelling and experimental validation of spiral mandrel die

The conventional theoretical method to calculate deformations and stress states is only limited to a few cases of simple extrusion dies due to a number of assumptions and simplifications. A coupled thermal-structural modelling framework incorporating finite element method is thus developed and implemented to determine the mechanical performances of the complicated spiral mandrel die, which has a complex geometrical feature of spiral grooves and is exposed to severe conditions of thermal load and high pressure. The steady-state thermal analysis is carried out by mapping the temperature load on the flow channel from previously simulated flow characteristics of polymer melt. The structural analysis takes inputs from both thermal analysis and previously simulated pressure on polymer melt. Both the temperature and pressure loads on flow channel are transferred via the Smart Bucket Surface mapping algorithm. The mechanical properties of the spiral mandrel die are evaluated by analysing the deformation and stress distribution. The experimental validation is conducted to demonstrate the effectiveness of the numerical model. The effects of both structure parameters of the spiral mandrel and processing parameters upon the maximum stress in the die body and the maximum pressure induced deformation at the die orifice are investigated