A computer-implemented method includes receiving multimodal data. The computer-implemented method further includes generating one or more kernel matrices from the multimodal data. The computer-implemented method further includes generating an equivalent kernel matrix using one or more coefficient matrices, wherein the one or more coefficient matrices are constrained by a nuclear norm. The computer-implemented method further includes initiating one or more iterative processes. Each of the one or more iterative processes includes: calculating an error for the one or more coefficient matrices of the equivalent kernel matrix based on a training set, and initiating a line search for the one or more coefficient matrices of the equivalent kernel matrix. The computer-implemented method further includes, responsive to generating an optimal coefficient matrix, terminating the one or more iterative processes. The method may be embodied in a corresponding computer system or computer program product.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: receiving multimodal data; generating one or more kernel matrices from said multimodal data; generating an equivalent kernel matrix using one or more coefficient matrices, said one or more coefficient matrices constrained by a nuclear norm; initiating one or more iterative processes, each of said one or more iterative processes comprising: calculating an error for said one or more coefficient matrices of said equivalent kernel matrix based on a training set; initiating a line search for said one or more coefficient matrices of said equivalent kernel matrix; and responsive to generating an optimal coefficient matrix, terminating said one or more iterative processes. 2. The computer-implemented method of claim 1, wherein said equivalent kernel matrix is a linear sum of products of said one or more kernel matrices and one or more coefficients. 3. The computer-implemented method of claim 1, wherein said nuclear norm of said one or more coefficient matrices is a value less than or equal to one. 4. The computer-implemented method of claim 1, wherein said error is a separation measure of said training set and is a function of said one or more coefficient matrices of said equivalent kernel matrix. 5. The computer-implemented method of claim 4, wherein said function of said one or more coefficient matrices of said equivalent kernel matrix is convex. 6. The computer-implemented method of claim 1, wherein said line search comprises gradient descent. 7. The computer-implemented method of claim 1, wherein said error is calculated using support vector machine software. 8. A computer program product, the computer program product comprising: one or more computer readable storage media and program instructions stored on said one or more computer readable storage media, said program instructions comprising instructions to: receive multimodal data; generate one or more kernel matrices from said multimodal data; generate an equivalent kernel matrix using one or more coefficient matrices, said one or more coefficient matrices constrained by a nuclear norm; initiate one or more iterative processes, each of said one or more iterative processes comprising: calculate an error for said one or more coefficient matrices of said equivalent kernel matrix based on a training set; initiate a line search for said one or more coefficient matrices of said equivalent kernel matrix; and responsive to generating an optimal coefficient matrix, terminate said one or more iterative processes. 9. The computer program product of claim 8, wherein said equivalent kernel matrix is a linear sum of products of said one or more kernel matrices and one or more coefficients. 10. The computer program product of claim 8, wherein said nuclear norm of said one or more coefficient matrices is a value less than or equal to one. 11. The computer program product of claim 8, wherein said error is a separation measure of said training set and is a function of said one or more coefficient matrices of said equivalent kernel matrix. 12. The computer program product of claim 11, wherein said function of said one or more coefficient matrices of said equivalent kernel matrix is convex. 13. The computer program product of claim 8, wherein said line search comprises gradient descent. 14. The computer program product of claim 8, wherein said error is calculated using support vector machine software. 15. A computer system, the computer system comprising: one or more computer processors; one or more computer readable storage media; computer program instructions; said computer program instructions being stored on said one or more computer readable storage media; said computer program instructions comprising instructions to: receive multimodal data; generate one or more kernel matrices from said multimodal data; generate an equivalent kernel matrix using one or more coefficient matrices, said one or more coefficient matrices constrained by a nuclear norm; initiate one or more iterative processes, each of said one or more iterative processes comprising: calculate an error for said one or more coefficient matrices of said equivalent kernel matrix based on a training set; initiate a line search for said one or more coefficient matrices of said equivalent kernel matrix; and responsive to generating an optimal coefficient matrix, terminate said one or more iterative processes. 16. The computer system of claim 15, wherein said equivalent kernel matrix is a linear sum of products of said one or more kernel matrices and said one or more coefficients. 17. The computer system of claim 15, wherein said nuclear norm of said one or more coefficient matrices is a value less than or equal to one. 18. The computer system of claim 15, wherein said error is a separation measure of said training set and is a function of said one or more coefficient matrices of said equivalent kernel matrix. 19. The computer system of claim 18, wherein said function of said one or more coefficient matrices of said equivalent kernel matrix is convex. 20. The computer system of claim 15, wherein said line search comprises gradient descent.