{"title":"Efficient Kernel Discriminant Analysis via Spectral Regression","authors":"Deng Cai, Xiaofei He, Jiawei Han","doi":"10.1109/icdm.2007.88","DOIUrl":"https://doi.org/10.1109/icdm.2007.88","url":null,"abstract":"Linear discriminant analysis (LDA) has been a popular method for extracting features which preserve class separability. The projection vectors are commonly obtained by maximizing the between class covariance and simultaneously minimizing the within class covariance. LDA can be performed either in the original input space or in the reproducing kernel Hilbert space (RKHS) into which data points are mapped, which leads to Kernel Discriminant Analysis (KDA). When the data are highly nonlinear distributed, KDA can achieve better performance than LDA. However, computing the projective functions in KDA involves eigen-decomposition of kernel matrix, which is very expensive when a large number of training samples exist. In this paper, we present a new algorithm for kernel discriminant analysis, called spectral regression kernel discriminant analysis (SRKDA). By using spectral graph analysis, SRKDA casts discriminant analysis into a regression framework which facilitates both efficient computation and the use of regularization techniques. Specifically, SRKDA only needs to solve a set of regularized regression problems and there is no eigenvector computation involved, which is a huge save of computational cost. Our computational analysis shows that SRKDA is 27 times faster than the ordinary KDA. Moreover, the new formulation makes it very easy to develop incremental version of the algorithm which can fully utilize the computational results of the existing training samples. Experiments on face recognition demonstrate the effectiveness and efficiency of the proposed algorithm.","PeriodicalId":233758,"journal":{"name":"Seventh IEEE International Conference on Data Mining (ICDM 2007)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129354771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Mining Interpretable Human Strategies: A Case Study","authors":"Xiaoli Z. Fern, Chaitanya Komireddy, M. Burnett","doi":"10.1109/ICDM.2007.19","DOIUrl":"https://doi.org/10.1109/ICDM.2007.19","url":null,"abstract":"This paper focuses on mining human strategies by observing their actions. Our application domain is an HCI study aimed at discovering general strategies used by software users and understanding how such strategies relate to gender and success. We cast this as a sequential pattern discovery problem, where user strategies are manifested as sequential patterns. Problematically, we found that the patterns discovered by standard algorithms were difficult to interpret and provided limited information about high-level strategies. To help interpret the patterns and extract general strategies, we examined multiple ways of clustering the patterns into meaningful groups, which collectively led to interesting findings about user behavior both in terms of gender differences and problem-solving success. As a real-world application of data mining techniques, our work led to the discovery of new strategic patterns that are linked to user success and had not been revealed in more than nine years of manual empirical work. As a case study, our work highlights important research directions for making data mining more accessible to non-experts.","PeriodicalId":233758,"journal":{"name":"Seventh IEEE International Conference on Data Mining (ICDM 2007)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133710758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Tutorials and Their Descriptions","authors":"Guozhu Dong","doi":"10.1109/icdm.2007.115","DOIUrl":"https://doi.org/10.1109/icdm.2007.115","url":null,"abstract":"Provides an abstract for each of the tutorial presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings.","PeriodicalId":233758,"journal":{"name":"Seventh IEEE International Conference on Data Mining (ICDM 2007)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131500203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}