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 Compound Subspace Method (CSM) [11]P (ω|X) =P (ω)p(X|ω)∑ω P (ω)p(X|ω) ∼n ∑T∑r∑k=1 f=1 i=1f w i ( f u ⊤ i f x k ) 2Multiple Kernel Learning (52)
69 (compound Bayesian decision problem) [12]P (ω|X) =P (ω)p(X|ω)∑ω P (ω)p(X|ω) n : X = (x 1 |x 2 | · · · |x n )n (context): ω = (ω(1), ..., ω(n)) ⊤ x i ω(i) カレイc⃝カレイひらめ(53)ひらめ
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 and 50: 69 CPU 1.86GHz 2GB32bit WindowsMAT
Page 51: 69 犬体のパターンを観
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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