A method of object recognition uses a convolutional neural network (CNN) trained with a set of training images [100, 104], then classifies [110] an image [108] of an object using the trained CNN. A first layer of the CNN is trained [102] by generating a set of first convolutional filters from eigenvectors produced from linear principal component analysis of patches of the training images [100]. The training of each of multiple hidden layers CNN includes generating a set of convolutional filters from a selected subset of eigenvectors produced from linear principal component analysis of patches of an affinity matrix constructed using a set of prior convolutional filters from a prior layer of the CNN, where the affinity matrix represents correlations of feature vectors associated with the prior layer. The last layer of the CNN is trained [106] with a regular classifier by error back-propagation using the training images and labels associated with the training images [104].
OBJECT RECOGNITION USING A CONVOLUTIONAL NEURAL NETWORK TRAINED BY PRINCIPAL COMPONENT ANALYSIS AND REPEATED SPECTRAL CLUSTERING