DermaMNIST statistics and PCA

DermaMNIST (64x64 resolution) mean (mean_reshaped.npy) and standard deviation (std_reshaped.npy) calculated on the training set; DermaMNIST's covariance matrix's (64x64 resolution) eigenvalues (eigenvalues.npy), the ratio of total variance explained by each principal component (eigenvalues_ratio.npy) and DermaMNIST's principal components (pc_matrix.npy) computed using the normalized training dataset. These items were computed from the DermaMNIST dataset [1] and there used in [2]. [1] Yang, Jiancheng, et al. "Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification." Scientific Data 10.1 (2023): 41. [2] Alice Bizeul, Thomas M. Sutter, Alain Ryser, Julius Von Kügelgen, Bernhard Schölkopf, Julia E. Vogt. Components Beat Patches: Eigenvector Masking for Visual Representation Learning. Oct, 2024.DermaMNIST（分辨率64×64）在训练集上计算得到的均值（存储为mean_reshaped.npy）与标准差（存储为std_reshaped.npy）；基于归一化训练数据集计算得到的DermaMNIST协方差矩阵（分辨率64×64）的特征值（存储为eigenvalues.npy）、各主成分所解释的总方差占比（存储为eigenvalues_ratio.npy），以及DermaMNIST的主成分矩阵（存储为pc_matrix.npy）。上述内容均源自DermaMNIST数据集[1]，并被文献[2]所采用。 [1] Yang, Jiancheng, et al. "MedMNIST v2: A large-scale lightweight benchmark for 2D and 3D biomedical image classification". Scientific Data 10.1 (2023): 41. [2] Alice Bizeul, Thomas M. Sutter, Alain Ryser, Julius Von Kügelgen, Bernhard Schölkopf, Julia E. Vogt. Components Beat Patches: Eigenvector Masking for Visual Representation Learning. October 2024.