2015-08-26

Two papers are accepted by ICCT2015

Supervised Hessian Eigenmap for Dimensionality Reduction

Abstract: Hessian Eigenmap is one proposed technique for dimensionality reduction. Many methods, such as ISOMAP, LLE, Laplacian Eigenmap, have been proposed under manifold learning for dimensionality reduction. However, all these ideas have not taken the influence of different class into consideration, which limit the effectiveness of manifold learning. To take account for the influence for multiclass and improve the performance of dimensional reduction, we proposed a new method, supervised Hessian LLE(SHLLE). To evaluate the proposed method, extensive experiments were conducted on the artificial dataset and real dataset(COIL-20). Our result demonstrate that the proposed method outperform HLLE method.

Keywords: Manifold Learning; Locally Linear Embedding; Hessian Eigenmap; Supervised Learning


Density Peak based Co-Spectral Clustering

Abstract: Spectral clustering employs spectral-graph structure of a similarity matrix to partition data into disjoint meaningful groups, because of its well-defined mathematical framework, good performance on arbitrary shaped clusters and simplicity, spectral clustering has gained considerable attentions in the recent past. Despite these virtues, spectral clustering suffers from several drawbacks, such as it is sensitive to initial condition, not robust to outliers and unable to determine a reasonable cluster number and so on. In this paper, we present a new approach named density peak spectral clustering (DPSC) which combines spectral clustering with density peak clustering algorithm (DPCA) into a unified framework to solve these problems. Since multi-view data is common in clustering problem, to further bootstrap the clustering performance by using complementary information from different view, then we propose co-trained density peak spectral clustering (Co-DPSC) which is an extension of DPSC to multi-views based on the co-training idea. Experimental comparisons with a number of baselines on a toy and three real-world datasets show the effectiveness of our proposed DPSC and Co-DPSC algorithm.

Keywords: Spectral clustering; Density peak clustering; Multi-view; Co-training

2015-08-09

课题组获“麦芒杯”第一届全国研究生移动终端应用设计创新大赛三等奖。

2015-08-10,课题组研究生张连波获"麦芒杯"第一届全国研究生移动终端应用设计创新大赛三等奖。

全国研究生移动终端应用设计创新大赛(英文名称:China Graduate Contest on Application, Design and Innovation of Mobile-Terminal)(以下简称"大赛")是"全国研究生创新实践系列活动"赛事之一。大赛由教育部学位与研究生教育发展中心和中国科协青少年科技中心共同主办,由全国工程专业学位研究生教育指导委员会联合主办。
    第一届大赛由北京邮电大学承办,由中国通信学会、移动智能终端技术创新与产业联盟和中国移动互联网产业联盟共同协办,由华为终端(东莞)有限公司赞助冠名"麦芒杯",即本届大赛命名为"'麦芒杯'第一届全国研究生移动终端应用设计创新大赛"。
    第一届大赛由中国信息通信研究院泰尔终端实验室、北京邮电大学计算机学院和北京邮电大学软件学院提供技术支持。