高级英语-文献订阅-三峡大学图书馆

摘要： Incomplete time series classification (ITSC) is an important issue in time series analysis since temporal data often has missing values in practical applications. However, integrating imputation (replacing missing data) and classification within a model often rapidly amplifies the error from imputed values. Reducing this error propagation from imputation to classification remains a challenge. To this end, we propose an adversarial joint-learning recurrent neural network (AJ-RNN) for ITSC, an end-to-end model trained in an adversarial and joint learning manner. We train the system to categorize the time series as well as impute missing values. To alleviate the error introduced by each imputation value, we use an adversarial network to encourage the network to impute realistic missing values by distinguishing real and imputed values. Hence, AJ-RNN can directly perform classification with missing values and greatly reduce the error propagation from imputation to classification, boosting the accuracy. Extensive experiments on 68 synthetic datasets and 4 real-world datasets from the expanded UCR time series archive demonstrate that AJ-RNN achieves state-of-the-art performance. Furthermore, we show that our model can effectively alleviate the accumulating error problem through qualitative and quantitative analysis based on the trajectory of the dynamical system learned by the RNN. We also provide an analysis of the model behavior to verify the effectiveness of our approach.

SCHAIN-IRAM: An Efficient and Effective Semi-Supervised Clustering Algorithm for Attributed Heterogeneous Information Networks

Li, Xiang Wu, Yao Ester, Martin Kao, Ben Wang, Xin Zheng, Yudian

Univ Hong Kong Dept Comp Sci Hong Kong Peoples R ChinaTwitter San Francisco CA 94103 USASimon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 CanadaTsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China

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关键词： Clustering algorithms Task analysis Facebook Data mining Linear programming Data models Computer science Semi-supervised clustering attributed heterogeneous information network object attributes network structure

摘要： A heterogeneous information network (HIN) is one whose nodes model objects of different types and whose links model objects' relationships. To enrich its information, objects in an HIN are typically associated with additional attributes. We call such an HIN an Attributed HIN or AHIN. We study the problem of clustering objects in an AHIN, taking into account objects' similarities with respect to both object attribute values and their structural connectedness in the network. We show how supervision signal, expressed in the form of a must-link set and a cannot-link set, can be leveraged to improve clustering results. We put forward the SCHAIN algorithm to solve the clustering problem, and two highly efficient variants, SCHAIN-PI and SCHAIN-IRAM, which employ the power iteration based method and the implicitly restarted Arnoldi method respectively to compute eigenvectors of a matrix. We conduct extensive experiments comparing SCHAIN-based algorithms with other state-of-the-art clustering algorithms. Our results show that SCHAIN-IRAM outperforms other competitors in terms of clustering effectiveness and is highly efficient.

计算机科学与技术GIS课程的教学实践

李辉

西安思源学院陕西710038

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