例題ではじめる部分空間法 - パターン認識へのいざない - - 東京農工大学

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69 (%) (s) (KB) 96.5 0.003 5140.2 97.1 0.002 5140 92.0 0.002 532 85.8 3 × 10 −5 20 97.4 0.01 9298 (r = 13) 96.9 6 × 10 −4 260 (¯λ = 0.95) 94.3 5 × 10 −4 231 SVM 93.9 136.8 3656 SVM 98.0 222 5352(50)
69 犬体のパターンを観測しても何であるかはわからない単同じクラスから由来する複数のパターン (CSM): (MSM): (51)
Page 2 and 3: 69 ()MATLAB/Octave ·3 download
Page 4: 別辞書識別部識 69 [1]
Page 7 and 8: 69 [2, 3, 4] : () · ()(7)
Page 9 and 10: 69 : minu i−d∑p(u i ) log p(u i
Page 11 and 12: 69 1/3 [5]0 “” subspacesub (
Page 13 and 14: 69 3/3線型部分空間で
Page 15 and 16: 69 r d u 1 , u 2 , ..., u r d ×
Page 17 and 18: 69 argmaxsubject to argmaxsubject t
Page 19 and 20: 69 () A u ⊤ u = 1 u ⊤ Au max u
Page 21 and 22: 69 n d x i (i = 1, ..., n) C j
Page 23 and 24: 69 cos θ 1/3U d × r 1 x U U
Page 25 and 26: 69 cos θ 3/3 c = (U ⊤ x)/∥U
Page 27 and 28: 69 U 2/2n x 1 , ..., x n d i =
Page 29 and 30: 69 N = X ⊤ X ∈ R n×n r 0
Page 31 and 32: 69 ω j N (x|µ j , Σ j ) =1(2π)
Page 33 and 34: 69 ˆµ j = 0, ˆΣj = R j R j = 1
Page 35 and 36: 69 g j (x) = −∥Λ − 1 2jU ⊤
Page 37 and 38: 69 1/w 1 ≤ 1/w 2 ≤ · · · ≤
Page 39 and 40: 69 r∑S j (x) = (r − i + 1)(u
Page 41 and 42: 69 makedata.m makedata.m USPS [
Page 43 and 44: 69 : i j ij class 0 1 2 3 4 5 6 7
Page 45 and 46: 69 Figure 1 (45)
Page 47 and 48: 69 ¯λ = ∑ ri=1 λ i/ ∑ rank(
Page 49: 69 CPU 1.86GHz 2GB32bit WindowsMAT
Page 53 and 54: 69 (compound Bayesian decision pro
Page 55 and 56: 69 CSMn x 1 , ..., x n i.i.d. P (
Page 57 and 58: 69 CSMn X T P (ω j | ¯X ) =T
Page 59 and 60: 69 V ∈ R d×r d: r d < d U ∈ R
Page 61 and 62: 69 2/2 (4) b ⊤ (5) c ⊤ λ =
Page 63 and 64: dog1 dog1-000-000.pngdog1-022-000.p
Page 65 and 66: 69 make_data.m Y = 0.2989R + 0.587
Page 67 and 68: 69 (doubutsu.mat) 33157331736IMG(:
Page 69 and 70: 69 CPU 2.8GHz 3.5GB32bit WindowsMA
Page 71 and 72: 69 ( d = 2) local subspace classif
Page 73 and 74: 69 II[8] - -1999.[9] , --, , 20
Page 75: 69 2 2: Au = λu (u ≠ 0) u A

例題ではじめる部分空間法 - パターン認識へのいざない - - 東京農工大学

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