Systems and methods are disclosed for operating a machine, by receiving training data from one or more sensors; training a machine learning module with the training data by: partitioning a data matrix into smaller submatrices to process in parallel and optimized for each processing node; for each submatrix, performing a greedy search for rank-one solutions; using alternating direction method of multipliers (ADMM) to ensure consistency over different data blocks; and controlling one or more actuators using live data and the learned module during operation.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for operating a machine, comprising: receiving training data from one or more sensors; training a machine learning module with the training data by: partitioning a large-scale data matrix into smaller submatrices; for each submatrix, greedily searching for rank-one solutions of Nonnegative matrix factorization/completion (NMFC) based upon alternating direction method of multipliers (ADMM); concatenating the sub-matrices to form low rank matrix factors; determining solutions of each subproblem and concatenated to form a final solution; and controlling one or more actuators using live data and the learned module during operation. 2. The method of claim 1, wherein the partitioning comprises selecting using one of random, rank-one singular value decomposition (SVD), and rank-one ADMM. 3. The method of claim 1, wherein computation is only linearly proportional to a matrix rank. 4. The method of claim 1, wherein for each submatrix X(i) with i as an index of submatrices, using rank-one ADMM to optimize NMFC over a node. 5. The method of claim 1, comprising uniformly selecting a number of samples for a node. 6. The method of claim 1, comprising performing SVD by projecting an eigenvector over the nonnegative set, and then conducting k-means clustering over the eigenvector. 7. The method of claim 1, comprising performing ADMM for NMFC by conducting k-means clustering over an eigenvector. 8. The method of claim 1, comprising providing a warm start for a second rank optimization. 9. The method of claim 1, wherein for rank-one ADMM, comprising performing a greedy search for rank-one solutions based upon ADMM. 10. The method of claim 1, comprising determining: { u  ( i ) k ← [ X ^  ( i )  ( v  ( i ) k ) T + ρ   a  ( i ) k - p ] [ v  ( i ) k  ( v  ( i ) k ) T + ρ ] v  ( i ) k ← [ u  ( i ) k T  X ^  ( i ) + ρ   b  ( i ) k + ∑ j ≠ i  ρ   v  ( j ) k - q - ( c - 1 )  s ] [ ( u  ( i ) k ) T  u  ( i ) k + ρ ] a  ( i ) k ← P +  ( u  ( i ) k + p / ρ ) b  ( i ) k ← P +  ( v  ( i ) k + q / ρ ) p ← p + ρ  ( u  ( i ) k - a  ( i ) k ) q ← q + ρ  ( v  ( i ) k - b  ( i ) k ) s ← s + ρ c - 1  ∑ j ≠ i  ( v  ( i ) k - v  ( j ) k ) where X is a partially observed data matrix of n samples and d features, i is an index, c is a constant, p, q and t are Lagrangian multipliers, p is a penalty operator, and U and V are nonnegative low rank matrix factors. 11. A method for operating a machine, comprising receiving training data from one or more sensors; training a machine learning module with the training data by: partitioning a data matrix into smaller submatrices to process in parallel and optimized for each processing node; for each submatrix, performing a greedy search for rank-one solutions; using alternating direction method of multipliers (ADMM) to ensure consistency over different data blocks; and controlling one or more actuators using live data and the learned module during operation. 12. The method of claim 11, comprising partitioning the data matrix into smaller submatrices using one of: random, rank-one SVD, and rank-one ADMM technique. 13. The method of claim 11, wherein for each submatrix X(i) with i as an index of submatrices, using rank-one ADMM to optimize NMFC over a node. 14. The method of claim 11, comprising uniformly selecting a number of samples for a node. 15. The method of claim 1, comprising performing SVD by projecting an eigenvector over the nonnegative set, and then conducting k-means clustering over the eigenvector. 16. The method of claim 1, comprising performing ADMM for NMFC by conducting k-means clustering over an eigenvector. 17. The method of claim 1, comprising providing a warm start for a second rank optimization. 18. The method of claim 1, wherein for rank-one ADMM, comprising performing a greedy search for rank-one solutions based upon ADMM. 19. The method of claim 1, comprising determining: { u  ( i ) k ← [ X ^  ( i )  ( v  ( i ) k ) T + ρ   a  ( i ) k - p ] [ v  ( i ) k  ( v  ( i ) k ) T + ρ ] v  ( i ) k ← [ u  ( i ) k T  X ^  ( i ) + ρ   b  ( i ) k + ∑ j ≠ i  ρ   v  ( j ) k - q - ( c - 1 )  s ] [ ( u  ( i ) k ) T  u  ( i ) k + ρ ] a  ( i ) k ← P +  ( u  ( i ) k + p / ρ ) b  ( i ) k ← P +  ( v  ( i ) k + q / ρ ) p ← p + ρ  ( u  ( i ) k - a  ( i ) k ) q ← q + ρ  ( v  ( i ) k - b  ( i ) k ) s ← s + ρ c - 1  ∑ j ≠ i  ( v  ( i ) k - v  ( j ) k ) where X is a partially observed data matrix of n samples and d features, i is an index, c is a constant, p, q and t are Lagrangian multipliers, ρ is a penalty operator, and U and V are nonnegative low rank matrix factors.