python - 在 Python 中实现 PCA(基于特征向量)

Question

我尝试用 Python 实现 PCA。我的目标是创建一个类似于 Matlab 的 PCA 实现的版本。但是，我认为我错过了一个关键点，因为我的测试部分产生了错误符号 (+/-) 的结果。

你能找出算法的错误吗？为什么标志有时不同？

基于特征向量的 PCA 实现:

new_array_rank=4
A_mean = np.mean(A, axis=0)    
A = A - A_mean    

covariance_matrix = np.cov(A.T)    

eigen_values, eigen_vectors = np.linalg.eig(covariance_matrix)
new_index = np.argsort(eigen_values)[::-1]
eigen_vectors = eigen_vectors[:,new_index]
eigen_values = eigen_values[new_index]

eigen_vectors = eigen_vectors[:,:new_array_rank]
return np.dot(eigen_vectors.T, A.T).T

我的测试值:

array([[ 0.13298325,  0.2896928 ,  0.53589224,  0.58164269,  0.66202221,
     0.95414116,  0.03040784,  0.26290471,  0.40823539,  0.37783385],
   [ 0.90521267,  0.86275498,  0.52696221,  0.15243867,  0.20894357,
     0.19900414,  0.50607341,  0.53995902,  0.32014539,  0.98744942],
   [ 0.87689087,  0.04307512,  0.45065793,  0.29415066,  0.04908066,
     0.98635538,  0.52091338,  0.76291385,  0.97213094,  0.48815925],
   [ 0.75136801,  0.85946751,  0.10508436,  0.04656418,  0.08164919,
     0.88129981,  0.39666754,  0.86325704,  0.56718669,  0.76346602],
   [ 0.93319721,  0.5897521 ,  0.75065047,  0.63916306,  0.78810679,
     0.92909485,  0.23751963,  0.87552313,  0.37663086,  0.69010429],
   [ 0.53189229,  0.68984247,  0.46164066,  0.29953259,  0.10826334,
     0.47944168,  0.93935082,  0.40331874,  0.18541041,  0.35594587],
   [ 0.36399075,  0.00698617,  0.61030608,  0.51136309,  0.54185601,
     0.81383604,  0.50003674,  0.75414875,  0.54689801,  0.9957493 ],
   [ 0.27815017,  0.65417397,  0.57207255,  0.54388744,  0.89128334,
     0.3512483 ,  0.94441934,  0.05305929,  0.77389942,  0.93125228],
   [ 0.80409485,  0.2749575 ,  0.22270875,  0.91869706,  0.54683128,
     0.61501493,  0.7830902 ,  0.72055598,  0.09363186,  0.05103846],
   [ 0.12357816,  0.29758902,  0.87807485,  0.94348706,  0.60896429,
     0.33899019,  0.36310027,  0.02380186,  0.67207071,  0.28638936]])

我的具有特征向量的 PCA 结果:

array([[  5.09548931e-01,  -3.97079651e-01,  -1.47555867e-01,
     -3.55343967e-02,  -4.92125732e-01,  -1.78191399e-01,
     -3.29543974e-02,   3.71406504e-03,   1.06404170e-01,
     -1.66533454e-16],
   [ -5.15879041e-01,   6.40833419e-01,  -7.54601587e-02,
     -2.00776798e-01,  -7.07247669e-02,   2.68582368e-01,
     -1.66124362e-01,   1.03414828e-01,   7.76738500e-02,
      5.55111512e-17],
   [ -4.42659342e-01,  -5.13297786e-01,  -1.65477203e-01,
      5.33670847e-01,   2.00194213e-01,   2.06176265e-01,
      1.31558875e-01,  -2.81699724e-02,   6.19571305e-02,
     -8.32667268e-17],
   [ -8.50397468e-01,   5.14319846e-02,  -1.46289906e-01,
      6.51133920e-02,  -2.83887201e-01,  -1.90516618e-01,
      1.45748370e-01,   9.49464768e-02,  -1.05989648e-01,
      4.16333634e-17],
   [ -1.61040296e-01,  -3.47929944e-01,  -1.19871598e-01,
     -6.48965493e-01,   7.53188055e-02,   1.31730340e-01,
      1.33229858e-01,  -1.43587499e-01,  -2.20913989e-02,
     -3.40005801e-16],
   [ -1.70017435e-01,   4.22573148e-01,   4.81511942e-01,
      2.42170125e-01,  -1.18575764e-01,  -6.87250591e-02,
     -1.20660307e-01,  -2.22865482e-01,  -1.73666882e-02,
     -1.52655666e-16],
   [  6.90841779e-02,  -2.86233901e-01,  -4.16612350e-01,
      9.38935057e-03,   3.02325120e-01,  -1.61783482e-01,
     -3.55465509e-01,   1.15323059e-02,  -5.04619674e-02,
      4.71844785e-16],
   [  5.26189089e-01,   6.81324113e-01,  -2.89960115e-01,
      2.01781673e-02,   3.03159463e-01,  -2.11777986e-01,
      2.25937548e-01,  -5.49219872e-05,   3.66268329e-02,
     -1.11022302e-16],
   [  6.68680313e-02,  -2.99715813e-01,   8.53428694e-01,
     -1.30066853e-01,   2.31410283e-01,  -1.02860624e-01,
      1.95449586e-02,   1.30218425e-01,   1.68059569e-02,
      2.22044605e-16],
   [  9.68303353e-01,   4.80944309e-02,   2.62865615e-02,
      1.44821658e-01,  -1.47094421e-01,   3.07366196e-01,
      1.91849667e-02,   5.08517759e-02,  -1.03558238e-01,
      1.38777878e-16]])

使用Matlab的PCA函数对相同数据的测试结果:

array([[ -5.09548931e-01,   3.97079651e-01,   1.47555867e-01,
      3.55343967e-02,  -4.92125732e-01,  -1.78191399e-01,
     -3.29543974e-02,  -3.71406504e-03,  -1.06404170e-01,
     -0.00000000e+00],
   [  5.15879041e-01,  -6.40833419e-01,   7.54601587e-02,
      2.00776798e-01,  -7.07247669e-02,   2.68582368e-01,
     -1.66124362e-01,  -1.03414828e-01,  -7.76738500e-02,
     -0.00000000e+00],
   [  4.42659342e-01,   5.13297786e-01,   1.65477203e-01,
     -5.33670847e-01,   2.00194213e-01,   2.06176265e-01,
      1.31558875e-01,   2.81699724e-02,  -6.19571305e-02,
     -0.00000000e+00],
   [  8.50397468e-01,  -5.14319846e-02,   1.46289906e-01,
     -6.51133920e-02,  -2.83887201e-01,  -1.90516618e-01,
      1.45748370e-01,  -9.49464768e-02,   1.05989648e-01,
     -0.00000000e+00],
   [  1.61040296e-01,   3.47929944e-01,   1.19871598e-01,
      6.48965493e-01,   7.53188055e-02,   1.31730340e-01,
      1.33229858e-01,   1.43587499e-01,   2.20913989e-02,
     -0.00000000e+00],
   [  1.70017435e-01,  -4.22573148e-01,  -4.81511942e-01,
     -2.42170125e-01,  -1.18575764e-01,  -6.87250591e-02,
     -1.20660307e-01,   2.22865482e-01,   1.73666882e-02,
     -0.00000000e+00],
   [ -6.90841779e-02,   2.86233901e-01,   4.16612350e-01,
     -9.38935057e-03,   3.02325120e-01,  -1.61783482e-01,
     -3.55465509e-01,  -1.15323059e-02,   5.04619674e-02,
     -0.00000000e+00],
   [ -5.26189089e-01,  -6.81324113e-01,   2.89960115e-01,
     -2.01781673e-02,   3.03159463e-01,  -2.11777986e-01,
      2.25937548e-01,   5.49219872e-05,  -3.66268329e-02,
     -0.00000000e+00],
   [ -6.68680313e-02,   2.99715813e-01,  -8.53428694e-01,
      1.30066853e-01,   2.31410283e-01,  -1.02860624e-01,
      1.95449586e-02,  -1.30218425e-01,  -1.68059569e-02,
     -0.00000000e+00],
   [ -9.68303353e-01,  -4.80944309e-02,  -2.62865615e-02,
     -1.44821658e-01,  -1.47094421e-01,   3.07366196e-01,
      1.91849667e-02,  -5.08517759e-02,   1.03558238e-01,
     -0.00000000e+00]])

Answer 1

特征向量的符号和其他规范化选择是任意的。 Matlab 和 numpy 以相同的方式规范特征向量，但符号是任意的，并且可以取决于所使用的线性代数库的细节。

当我编写与 matlab 的 princomp 等效的 numpy 时，当我在单元测试中将它们与 matlab 的特征向量进行比较时，我只是将特征向量的符号归一化。