nmanydataanalysistasks,oneisoftenconfrontedwithveryhighdimensionaldata.Featureselectiontechniquesaredesignedtofindtherelevantfeaturesubsetoftheoriginalfeatureswhichcanfacilitateclustering,classificationandretrieval.Thefeatureselectionproblemisessentiallyacombinatorialoptimizationproblemwhichiscomputationallyexpensive.Traditionalfeatureselectionmethodsaddressthisissuebyselectingthetoprankedfeaturesbasedoncertainscorescomputedindependentlyforeachfeature.Theseapproachesneglectthepossiblecorrelationbetweendifferentfeaturesandthuscannotproduceanoptimalfeaturesubset.InspiredfromtherecentdevelopmentsonmanifoldlearningandL1-regularizedmodelsforsubsetselection,weproposehereanewapproach,called{\emMulti-Cluster/ClassFeatureSelection}(MCFS),forfeatureselection.Specifically,weselectthosefeaturessuchthatthemulti-cluster/classstructureofthedatacanbebestpreserved.Thecorrespondingoptimizationproblemcanbeefficientlysolvedsinceitonlyinvolvesasparseeigen-problemandaL1-regularizedleastsquaresproblem.ItisimportanttonotethatMCFScanbeappliedinsuperised,unsupervisedandsemi-supervisedcases.Ifyoufindthesealgoirthmsuseful,weappreciateitverymuchifyoucanciteourfollowingworks:PapersDengCai,ChiyuanZhang,XiaofeiHe,"UnsupervisedFeatureSelectionforMulti-clusterData",16thACMSIGKDDConferenceonKnowledgeDiscoveryandDataMining(KDD'10),July2010.BibtexsourceXiaofeiHe,DengCai,andParthaNiyogi,"LaplacianScoreforFeatureSelection",AdvancesinNeuralInformationProcessingSystems18(NIPS'05),Vancouver,Canada,2005Bibtexsource

NSF-PROD-NIPS-V5.6-WH(冰之眼白皮书)

Attentionisallyouneed

在日常工作中，钉钉打卡成了我生活中不可或缺的一部分。然而，有时候这个看似简单的任务却给我带来了不少烦恼。 每天早晚，我总是得牢记打开钉钉应用，点击"工作台"，再找到"考勤打卡"进行签到。有时候因为工作忙碌，会忘记打卡，导致考勤异常，影响当月的工作评价。而且，由于我使用的是苹果手机，有时候系统更新后，钉钉的某些功能会出现异常，使得打卡变得更加麻烦。 另外，我的家人使用的是安卓手机，他们也经常抱怨钉钉打卡的繁琐。尤其是对于那些不太熟悉手机操作的长辈来说，每次打卡都是一次挑战。他们总是担心自己会操作失误，导致打卡失败。 为了解决这些烦恼，我开始思考是否可以通过编写一个全自动化脚本来实现钉钉打卡。经过一段时间的摸索和学习，我终于成功编写出了一个适用于苹果和安卓系统的钉钉打卡脚本。