[0043]To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application.
[0044]Such asfigure 1 orfigure 2As shown, the present invention discloses a migration learning method for heterogeneous users, which includes the following steps:
[0045]Step 1. Participants perform data collection and primary processing on the local side to achieve the first data dimensionality reduction.
[0046]Specifically, step 1 includes the following steps:
[0047]Step 1. Participants perform data collection and primary processing on the local side to achieve the first data dimensionality reduction.
[0048]Step 2. According to the needs of the participants, the server selects and delimits the source domain and the target domain to achieve the second data dimensionality reduction.
[0049]Step 3. Use the S-CNN circular classification algorithm for classification.
[0050]Further, as a preferred embodiment, the specific steps of step 1 are:
[0051]Step 1-1, participants use raw data X locallyn×h , Calculate the data covariance matrix F: Where n is the number of entries of the participant's local raw data, and h is the dimension of the data;
[0052]Step 1-2, according to |λE-F|=0, calculate all its eigenvalues λ and their corresponding eigenvectors μ, where E is the identity matrix;
[0053]Steps 1-3, for the characteristic value λi(λi∈λ) sort, and select the number of principal components according to a predetermined threshold r;
[0054]Step 1-4, output the feature vector set corresponding to the first r feature values (μ1,μ2,...,Μr), and calculate the modulus of the eigenvectors, unitize the corresponding r eigenvectors to form an eigen matrix A;
[0055]Step 1-5, calculate the projection matrix X'n×r=Xn×h A(r
[0056]Steps 1-6, the server receives and stores the locally reduced data set uploaded by all participants. Form a data pool Where X′vRepresents the sample data matrix uploaded by the v-th participant in the data pool, and N represents the number of participants; 3. A migration learning method for heterogeneous users according to claim 1, characterized in that: the specifics of step 2 The steps are:
[0057]Step 2-1, participant u uploads its classification requirements (Nu,Mu,accu), where NuIs the number of source domains, MuIs the number of categories, accuIndicates the lowest classification accuracy;
[0058]Step 2-2, the server calculates each data sample matrix X in the data poolv'Data sample uploaded with participant u XuThe correlation of 'is calculated as follows:
[0059]
[0060]Among them, I represents the data matrix Xv'And Xu'The correlation between; x'represents a set of data of the data matrix X'; P(xv',xu') means xv'And xu'Joint probability distribution of two sets of data; P(x) represents the probability distribution of data x; P(xv'|xu') means data xvIn data xuThe probability distribution of'; KL stands for distance, which is short for Kullback-Leibler difference;
[0061]Step 2-3, according to the correlation I(Xv',Xu'), for Xv'Sort from high to low, and select the top N with high relevance according to the needs of participantsuX as the source domain of this transfer learningS, The participant's sample data Xu'As target domain XT;
[0062]Steps 2-4, perform secondary dimensionality reduction: use the migration component analysis TCA algorithm to map data in multiple fields to the same dimensional space. Feature mapping is accompanied by a reduction in the number of features, and finally a new data feature sample will be obtained matrix.
[0063]Further, as a preferred embodiment, the specific steps of steps 2-4 are:
[0064]Step 2-4-1, define the kernel matrix K: calculate X separatelyS, XTAnd the kernel matrix K of the two composite domainsS,S , KT,T , KT,S And KS, T , And then use formula (1) to construct the kernel matrix K;
[0065]
[0066]Where K is a (n1+n2)×(n1+n2) Of the matrix, n1And n2Respectively XSAnd XTThe number of samples mapped to the regenerated nuclear Hilbert space (RKHS);
[0067]Step 2-4-2, using the empirical kernel mapping function, decompose the kernel matrix K into K=(KK-1/2)(K-1/2K);
[0068]Step 2-4-3, according to K, calculate the characteristic distance between the source domain and the target domain according to formula 2:
[0069]Dist(XS,XT)=tr(KL) (2)
[0070]Among them, tr(KL) represents the trace of the matrix KL.
[0071]Steps 2-4-4, calculate W according to equation 3:
[0072]
[0073]among them, Means XSAnd XTThe maximum mean error (MMD) distance between the empirical means of the two domains, namely XSAnd XTKL distance between two domains; Is the result of the empirical kernel mapping of the m-dimensional space of W;
[0074]Step 2-4-5, output the final source domain matrix WXS , And the target domain matrix WXT;
[0075]Further, as a preferred embodiment, the specific classification steps of step 3 are:
[0076]Step 3-1, initialization: first train WXS Label data to get the initialized Softmax classifier;
[0077]Step 3-2, from WXT Select a part of the samples in, and initialize the cycle discrimination times q of this batch of data to 0;
[0078]Step 3-3: Use the Softmax classifier to predict and label the unlabeled sample data, and add pseudo labels to it to obtain the initial classification result;
[0079]Step 3-4, also pass the batch of samples through the CNN classifier to obtain the binary classification result;
[0080]Step 3-5, compare the results of the primary classification with the results of the secondary classification; when the two are inconsistent, set q=q+1, and judge whether q is greater than the threshold Q, if so, delete this batch of data and return to step 3- 2. Otherwise, return to step 3-3;
[0081]Steps 3-6, use the sample data to train the Softmax classifier, obtain the classification accuracy acc and combine the classification accuracy acc with the demand accuracy acc of participant iuCompared; when the classification accuracy acc is greater than accu, Then output the classification accuracy acc and the classification result; otherwise, return to step 3-3.
[0082]The present invention adopts the above technical solutions. First, the server and other participants will not obtain the original data, which reduces the risk of privacy leakage to a certain extent. Secondly, through domain delimitation and secondary dimensionality reduction screening, the sample data has a higher correlation with the classification target, can adapt to the heterogeneity of users, has a better classification effect, and can greatly meet the needs of high classification accuracy. On the other hand, the cyclic double classification algorithm of Softmax and CNN, supervised learning guides unsupervised learning, and improves the classification accuracy of under-labeled data. The invention selects and delimits the source domain and the target domain for the data obtained through multiple channels at the local end, so as to ensure that the migration learning has a sufficient amount of data. On this basis, it meets the needs of multi-target output and improves the classification accuracy.
[0083]The present invention adopts the above technical solutions. First, the server and other participants will not obtain the original data, which reduces the risk of privacy leakage to a certain extent. Secondly, through domain delimitation and secondary dimensionality reduction screening, the sample data has a higher correlation with the classification target, can adapt to the heterogeneity of users, has a better classification effect, and can greatly meet the needs of high classification accuracy. On the other hand, the cyclic double classification algorithm of Softmax and CNN, supervised learning guides unsupervised learning, and improves the classification accuracy of under-labeled data. The invention selects and delimits the source domain and the target domain for the data obtained through multiple channels at the local end, so as to ensure that the migration learning has a sufficient amount of data. On this basis, it meets the needs of multi-target output and improves the classification accuracy.
[0084]Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. In the case of no conflict, the embodiments in the application and the features in the embodiments can be combined with each other. The components of the embodiments of the present application generally described and shown in the drawings herein may be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present application is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the present application. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of this application.
