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Ci Mi(Copiedfrom1主主成分分析---最大化方奇異值分析ICA[Hyvarneneta,2LDA（線性判別分析X -vaaSw( LDA（線性判別分析X -vaaSw( μi)(x μi)i=1x2!Bewee-vaaSB Ni( i=1ObecJ(W)DmesoeducSBwSw3[H.S.Seung&D.D.Lee.[Teebaumea,[RowesadSau,可解釋計算有效示例：平移人等度規(guī)映射等度規(guī)映射[Tenenbaumeta.,S Gveaegbooodsze$k$,compuepawsedsaceeacpoadsegbogpos（設(shè)定鄰域Sep2CompueGeodescdsacebeweefa-awayposbyuzggapdsace（計算圖上的測地線距離）Sep3EmpoyMudmesoascagocacuaesow-dmesoasubspace（利用多元尺度來計算 4LocayLnearEmbeddng[RowesandSau,實際效果LocayLnearEmbeddng[RowesandSau,實際效果維數(shù)約維數(shù)約稀疏學(xué)概率圖模深度學(xué)優(yōu)化技Occam‘srazor壓縮傳感壓縮傳感(CompressveSens6特征組合---Lasso/LARSL1Norm?=argmin|yXX|特征組合---Lasso/LARSL1Norm?=argmin|yXX|2 2||2 1|| 3B (x x稀疏 稀疏 特征表達(部件)非負矩陣分解[SeugadLee,深度學(xué)習簡介HebbDBN(DeepBeliefMultimodal維數(shù)深度學(xué)習簡介HebbDBN(DeepBeliefMultimodal維數(shù)約稀疏學(xué)概率圖模深度學(xué)HoraceBasilHoraceBasilBarlowBalow體。donthavemuchtmetoreadpapersanymore,evenwhenmdonthavemuchtmetoreadpapersanymore,evenwhenmstedasacoauthorGeoffHintonisalivinglegend.Healmostsingle-handedlyinventedbackpropagationfortrainingfeed-forwardneuralnetworks.ProfessorHintonrespondedbycreatingdeepnetworksanddeeplearning,anultimateformofmachinelearningRestrctedRestrctedBotzmannMachne(RBM)-淺SVMSupportVectorMachines/最大熵方法（如LR,Logistic?Hinton,Science,卷積神經(jīng)網(wǎng)絡(luò)（卷積神經(jīng)網(wǎng)絡(luò)（LeNet-5,LeCunYan,Y.LeCun,L.Bottou,Y.Bengio,andP.Haffner.Gradient-basedlearningappliedtoRecognition.ProceedingsoftheIEEE,Nov.1998.2012-A非線性激活函數(shù)：防過擬合：Dopou2012-A非線性激活函數(shù)：防過擬合：Dopou數(shù)據(jù)增引入max-poog技大數(shù)據(jù)訓(xùn)練202mageNe比賽第一雙GUNVDAGTX580顯著減少訓(xùn)練Knzhevsky,Alx,IlyaSutskever,andGeoffreyE.Hinton,Imagenetclassificationwithdeepconvolutionalneuralnetworks,NIPS,2012InfectInfectonLSTM(Long-ShortTermLSTM(Long-ShortTerm成功的原因之一(ICLR2017最 過擬合但卻能讓測試誤差繼續(xù)降模型復(fù)雜度的定義需要重新思(GeoffreyHnton,?物神經(jīng)元不可能進?數(shù)值