文件名称:CK-1_Repro.v1.02
- 所属分类：
- 图形图像处理(光照,映射..)
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2015-03-26
- 文件大小：
- 11.39mb
- 下载次数：
- 0次
- 提 供 者：
- 裴**
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
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有时间序列方法和技术的兴趣大增。从人，自然收集的信息几乎每一件，和生物过程是容易随时间的变化，以及这些变化如何发生的研究是一个中心问题充分理解这样的过程。所有的时间序列数据挖掘任务的分类中，可能是最突出的一个。在时间序列的分类有大量的实证研究，在时间域表明近邻规则是非常有效的。然而，一定的时间序列特征不在这个领域很容易地识别和表达的变化可能揭示了一些重要的和未知的特征。在这项工作中我们提出了递归图的使用对于时间序列的分类表示域。我们的方法复发措施地块使用坎帕纳基奥之间的相似性（CK-1）的距离，一个基于Kolmogorov复杂性的距离，利用视频压缩算法来估计图像的相似性。我们表明，递归图与CK-1距离导致比欧氏距离和动态时间翘曲在几个数据集的准确率显著提高。虽然复发地块不能用于所有的数据集提供准确率最高的，我们证明了我们可以预测未来的时间，我们的方法将跑赢时间表示欧氏距离和动态时间弯曲距离。-There is a huge increase of interest for time series methods and techniques. Virtually every piece of information collected human, natural, and biological processes is susceptible to changes over time, and the study of how these changes occur is a central issue to fully understand such processes. Among all time series mining tasks, classification is likely to be the most prominent one. In time series classification there is a significant body of empirical research that indicates that k-nearest neighbor rule in the time domain is very effective. However, certain time series features are not easily identified in this domain and a change in representation may reveal some significant and unknown features. In this work we propose the use of recurrence plots as representation domain for time series classification. Our approach measures the similarity between recurrence plots using Campana-Keogh (CK-1) distance, a Kolmogorov complexity-based distance that uses video compression algorithms to
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CK-1_Repro\compression
..........\...........\mfiles
..........\...........\......\comparison
..........\...........\......\..........\encoderOptionsTest.m
..........\...........\......\..........\gabor
..........\...........\......\..........\.....\bsmGaborFilterBank.m
..........\...........\......\..........\.....\bsmTextureDescriptor.m
..........\...........\......\..........\.....\pairwiseBSMDistances.m
..........\...........\......\..........\.....\pairwiseTextonBSMDistances.m
..........\...........\......\..........\.....\PlotHalfMagnitudes.m
..........\...........\......\..........\.....\showFilters.m
..........\...........\......\..........\maRetrievalPerformance.m
..........\...........\......\..........\scaleTest.m
..........\...........\......\..........\sift
..........\...........\......\..........\....\pairwiseSIFTDistances.m
..........\...........\......\..........\texton
..........\...........\......\..........\......\clusterResponses.m
..........\...........\......\..........\......\filterBankConv.m
..........\...........\......\..........\......\pairwiseTextonDistances.m
..........\...........\......\..........\timetest.m
..........\...........\......\..........\WhiteNoiseTest.m
..........\...........\......\compressionDistance
..........\...........\......\...................\cDistance.m
..........\...........\......\...................\compressionDendrogram.m
..........\...........\......\crossvalidations
..........\...........\......\................\fingerprintCrossValidation.m
..........\...........\......\................\folderSeperatedCrossValidation.m
..........\...........\......\................\genericCrossValidation.m
..........\...........\......\................\letterCrossValidation.m
..........\...........\......\................\mammogramCrossValidation.m
..........\...........\......\................\mammogramDataParser.m
..........\...........\......\................\mothCrossValidation.m
..........\...........\......\................\nematodeCrossValidation.m
..........\...........\......\................\outexCrossValidation.m
..........\...........\......\................\singleInstanceCrossValidation.m
..........\...........\......\................\spiderCrossValidation.m
..........\...........\......\................\tireCrossValidation.m
..........\...........\......\................\woodCAIROCrossValidation.m
..........\...........\......\................\XValLoadImages.m
..........\...........\......\................\XValPreprocessImages.m
..........\...........\......\mpegDistance
..........\...........\......\............\classifyKnn.m
..........\...........\......\............\compareMpegSizes.m
..........\...........\......\............\createClassBanner.m
..........\...........\......\............\createTestBanner.m
..........\...........\......\............\getBannerClassifications.m
..........\...........\......\............\getBannerDistance.m
..........\...........\......\............\getClassControlBannerSize.m
..........\...........\......\............\getMovieSize.m
..........\...........\......\............\getMpegSize.m
..........\...........\......\............\makeUncompressedAvi.m
..........\...........\......\............\mpegCompressionDendrogram.m
..........\...........\......\............\pairwiseMpegDistances.m
..........\...........\......\............\plotCompressionSizes.m
..........\...........\......\............\rotationInvariantSymetricMpegDistance.m
..........\...........\......\............\runOutexProblem_BannerDistance.m
..........\...........\......\............\symDistNET.prj
..........\...........\......\............\symDistNET
..........\...........\......\............\symetricdistancec
..........\...........\......\............\.................\win32
..........\...........\......\............\.................\.....\mccExcludedFiles.log
..........\...........\......\............\.................\.....\readme.txt
..........\...........\......\............\.................\.....\symetricMpegDistanceC.c
..........\...........\......\..