Please select the most similar patent number from A, B, C and D. Which number is?
Disclosed are a sound classification method, device and medium based on semi-nonnegative matrix factorization with constraint. The sound classification method comprises the following steps: representing a training sound data sample and a testing sound data sample as a semi-nonnegative matrix (S1); constructing a category constraint matrix and a sparse constraint matrix according to the semi-nonnegative matrix (S2); performing semi-nonnegative matrix factorization with constraint on the semi-nonnegative matrix under category constraint and sparse constraint to obtain a corresponding coefficient matrix; training a classification model to obtain a classifier by using low-dimensional representation in the coefficient matrix corresponding to the training sound data sample and category information of the training sound data sample as training data (S3); inputting the low-dimensional representation in the coefficient matrix corresponding to the testing sound data sample into the classifier, and outputting a classification result of the testing sound data sample (S4). The method makes effective use of the category information of the training sound data sample and enables the low-dimensional representation after dimension reduction to have sparsity, thereby obtaining the low-dimensional representation of samples with better discrimination and improving the accuracy of the sound data classification method.
A: A schizophrenia classification and identification method, an operation control apparatus, and medical equipment. The method comprises: acquiring a data sample, and preprocessing the data sample to obtain a functional connection matrix (S100); inputting the functional connection matrix into a convolutional layer of a multi-kernel capsule network, providing several convolutional kernels of different sizes in the convolutional layer, and extracting connection information from the functional connection matrix by means of each convolutional kernel to obtain capsules, wherein the capsules are represented in the form of vectors, the directions of the vectors represent attributes, and the lengths of the vectors represent the probabilities of the corresponding attributes (S200); inputting the capsules into a capsule layer of the multi-kernel capsule network, iteratively updating a weighting matrix on the basis of a random capsule inactivation strategy and by using a routing protocol algorithm, and then converting the capsules into prediction vectors by means of the weighting matrix (S300); carrying out weighted summation on the prediction vectors and then inputting same into a classification capsule layer of the multi-kernel capsule network, and according to an edge loss function, updating a network connection weight value by using a backpropagation algorithm (S400); and performing training according to the multi-kernel capsule network to obtain a final weight value, and then performing calculation according to the final weight value to obtain a classification result of the data sample, so as to classify schizophrenia therefrom. The method can realize the classification and identification of schizophrenia, improve the classification accuracy, and improve the performance and robustness of a classification method.
B: A lung sound classification method and system based on knowledge distillation, a terminal, and a storage medium. The method comprises: obtaining a lung sound audio, extracting Mel frequency spectrum features of the lung sound audio on the basis of the lung sound audio, and obtaining a lung sound data set (S100); training a plurality of teacher models on the basis of the lung sound data set to obtain a plurality of trained teacher models (S200); respectively calculating soft labels of the lung sound data set according to the plurality of trained teacher models, and performing conversion to obtain multi-classification soft labels (S300); and training a student model on the basis of the multi-classification soft labels and the lung sound data set to obtain a trained student model, and classifying the lung sound audio by utilizing the trained student model (S400). According to the method, on the basis of knowledge distillation, student model learning is guided by utilizing knowledge of a plurality of teacher models, so that multi-party lung sound audios are fully utilized, and the lung sound audios are accurately classified.
C: A remote sensing image feature extraction method employing tensor collaborative graph-based discriminant analysis comprises: taking each pixel as a center to extract a three-dimensional tensor data block; dividing, according to a certain ratio, experimental data into a training set and a test set; calculating a Euclidean distance between a current training pixel and each category of training data, and constructing a diagonal weight constraint matrix; then designing a constrained L2-norm collaborative representation model, and constructing a graph weight matrix and a tensor locality-preserving projection model; obtaining projection matrices for respective dimensions of a corresponding tensor data block; and finally, using a low-dimensional projection matrix to obtain a training set and a test set in the form of three-dimensional low-dimensional representations, inputting extracted low-dimensional features into a support vector machine classifier and classifying same by expanding feature dimensions into column vectors, determining a category of the test set, and evaluating feature extraction performance according to the classification result.
D: A face recognition construction method and system based on non-linear non-negative matrix decomposition, and a storage medium. The method comprises: depicting the degree of loss after matrix decomposition by using l  2,p -norm; enhancing the sparsity representation of a feature by using l  1 -norm of a matrix, and adding a regularization term regarding a matrix H into a loss function; constructing a target function F(W,H) by means of a degree of loss depicting step and a sparsity enhancement step; and obtaining an update iteration formula for kernel non-negative matrix decomposition of fractional power inner product. The method can solve the problem that a kernel non-negative matrix decomposition algorithm is sensitive to a singular value, can enhance the sparsity of the algorithm to feature representation, and also solves the problem that super parameters of a polynomial kernel function can only be integers.
Answer: B