So, this paper introduces the idea of sparse representation into the architecture of the deep learning network and comprehensively utilizes the sparse representation of well multidimensional data linear decomposition ability and the deep structural advantages of multilayer nonlinear mapping to complete the complex function approximation in the deep learning model. For the most difficult to classify OASIS-MRI database, all depth model algorithms are significantly better than traditional types of algorithms. However, while increasing the rotation expansion factor while increasing the in-class completeness of the class, it greatly reduces the sparsity between classes. In 2017, Sankaran et al. % images in imds to the size required by the network. IEEE Conference on. Therefore, this method became the champion of image classification in the conference, and it also laid the foundation for deep learning technology in the field of image classification. Section 3 systematically describes the classifier design method proposed in this paper to optimize the nonnegative sparse representation of kernel functions. In Top-1 test accuracy, GoogleNet can reach up to 78%. The basic flow chart of the proposed image classification algorithm is shown in Figure 4. Deep learning allows machines to … The KNNRCD method can combine multiple forms of kernel functions such as Gaussian Kernel and Laplace Kernel. It can increase the geometric distance between categories, making the linear indivisible into linear separable. Since the training samples are randomly selected, therefore, 10 tests are performed under each training set size, and the average value of the recognition results is taken as the recognition rate of the algorithm under the size of the training set. Computer Vision and Pattern Recognition, 2009. Second, the deep learning model comes with a low classifier with low accuracy. To extract useful information from these images and video data, computer vision emerged as the times require. Its training goal is to make the output signal approximate the input signal x, that is, the error value between the output signal and the input signal is the smallest. In particular, we will train our own small net to perform a rudimentary classification. % Use splitEachLabel method to trim the set. In order to achieve the purpose of sparseness, when optimizing the objective function, those which deviate greatly from the sparse parameter ρ are punished. Therefore, can be used to represent the activation value of the input vector x for the first hidden layer unit j, then the average activation value of j is. Then, in order to improve the classification effect of the deep learning model with the classifier, this paper proposes to use the sparse representation classification method of the optimized kernel function to replace the classifier in the deep learning model. It will build a deep learning model with adaptive approximation capabilities. Below are some applications of Multi Label Classification. It will improve the image classification effect. These applications require the manual identification of objects and facilities in the imagery. In this article we will be solving an image classification problem, where our goal will be to tell which class the input image belongs to. In this paper, the output of the last layer of SAE is used as the input of the classifier proposed in this paper, which keeps the parameters of the layers that have been trained unchanged. It can be seen from Table 2 that the recognition rate of the proposed algorithm is high under various rotation expansion multiples and various training set sizes. Specifically, this method has obvious advantages over the OverFeat  method. In CNNs, the nodes in the hidden layers don’t always share their output with every node in the next layer (known as convolutional layers). There are 96 individual sets of, % Get training labels from the trainingSet, % Train multiclass SVM classifier using a fast linear solver, and set, % 'ObservationsIn' to 'columns' to match the arrangement used for training, % Pass CNN image features to trained classifier. In order to further verify the classification effect of the proposed algorithm on general images, this section will conduct a classification test on the ImageNet database [54, 55] and compare it with the mainstream image classification algorithm. The deep learning algorithm proposed in this paper not only solves the problem of deep learning model construction, but also uses sparse representation to solve the optimization problem of classifier in deep learning algorithm. In the real world, because of the noise signal pollution in the target column vector, the target column vector is difficult to recover perfectly. It can be seen from Table 3 that the image classification algorithm based on the stacked sparse coding depth learning model-optimized kernel function nonnegative sparse representation is compared with the traditional classification algorithm and other depth algorithms. In this paper, the image in the ImageNet data set is preprocessed before the start of the experimental process, with a uniform size of 256 × 256. It is mainly divided into five steps: first, image preprocessing; second, initialize the network parameters and train the SAE layer by layer; third, a deep learning model based on stacked sparse autoencoder is established; fourth, establish a sparse representation classification of the optimized kernel function; fifth, test the model. The particle loss value required by the NH algorithm is li,t = r1. Therefore, the proposed algorithm has greater advantages than other deep learning algorithms in both Top-1 test accuracy and Top-5 test accuracy. But the calculated coefficient result may be . Among such tasks we have image classification: teaching a machine to recognize the At the same time, the mean value of each pixel on the training data set is calculated, and the mean value is processed for each pixel. % Visualize the first section of the network. Interactively fine-tune a pretrained deep learning network to learn a new image classification task. It is entirely possible to build your own neural network from the ground up in a matter of minutes wit… It will build a deep learning model with adaptive approximation capabilities. To further verify the universality of the proposed method. The classification of images in these four categories is difficult; even if it is difficult for human eyes to observe, let alone use a computer to classify this database. This paper also selected 604 colon image images from database sequence number 184.108.40.206.4.1.93220.127.116.11. 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