Provided are an apparatus and method for compressing a deep neural network (DNN). The DNN compression method includes receiving a matrix of a hidden layer or an output layer of a DNN, calculating a matrix representing a nonlinear structure of the hidden layer or the output layer, and decomposing the matrix of the hidden layer or the output layer using a constraint imposed by the matrix representing the nonlinear structure.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A deep neural network (DNN) compression method performed by at least one processor, the method comprising: receiving a matrix of a hidden layer or an output layer of a DNN; calculating a matrix representing a nonlinear structure of the hidden layer or the output layer; and decomposing the matrix of the hidden layer or the output layer using a constraint imposed by the matrix representing the nonlinear structure. 2. The DNN compression method of claim 1, wherein the calculating of the matrix includes expressing the nonlinear structure as a manifold structure and calculating the matrix. 3. The DNN compression method of claim 2, wherein the calculating of the matrix includes calculating the matrix representing the manifold structure using a Laplacian matrix. 4. The DNN compression method of claim 1, wherein the decomposing of the matrix includes decomposing the hidden layer or the output layer into matrices satisfying an expression below: minU,V(∥W−UV∥2+αTr(VBVT)) [Expression] (W: the hidden-layer or output-layer matrix, U and V: the matrices obtained by decomposing the hidden-layer or output-layer matrix, α: a Lagrange multiplier, and B: a Laplacian matrix representing a nonlinear structure of the DNN). 5. The DNN compression method of claim 4, wherein the decomposing of the hidden layer or the output layer into the matrices satisfying the above expression includes: calculating C according to C=(I+αB); decomposing C as C=DDT through a Cholesky decomposition; calculating W(DT)−1 with DT; decomposing WDT−1 as W(DT)−1≈EΣF; and calculating U=E, V=ETWC−1 using E. 6. A deep neural network (DNN) compression apparatus including at least one processor, wherein the processor comprises: an input portion configured to receive a matrix of a hidden layer or an output layer of a DNN; a calculator configured to calculate a matrix representing a nonlinear structure of the hidden layer or the output layer; and a decomposer configured to decompose the matrix of the hidden layer or the output layer using a constraint imposed by the matrix representing the nonlinear structure. 7. The DNN compression apparatus of claim 6, wherein the calculator expresses the nonlinear structure as a manifold structure and calculates the matrix. 8. The DNN compression apparatus of claim 7, wherein the calculator calculates the matrix representing the manifold structure using a Laplacian matrix. 9. The DNN compression apparatus of claim 6, wherein the decomposer decomposes the hidden layer or the output layer into matrices satisfying an expression below: minU,V(∥W−UV∥2+αTr(VBVT)) [Expression] (W: the hidden-layer or output-layer matrix, U and V: the matrices obtained by decomposing the hidden-layer or output-layer matrix, α: a Lagrange multiplier, and B: a Laplacian matrix representing a nonlinear structure of the DNN). 10. The DNN compression apparatus of claim 9, wherein the decomposer calculates the matrices U and V satisfying the above expression by calculating C according to C=(I+αB), decomposing C as C=DDT through a Cholesky decomposition, calculating W(DT)−1 with DT, decomposing WDT−1 as W(DT)−1≈EΣF, and calculating U=E, V=ETWC−1 using E.