A method and system are provided. The method includes determining from a data matrix having rows and columns, a clustering vector of the rows and a clustering vector of the columns. Each row in the clustering vector of the rows is a row instance and each row in the clustering vector of the columns is a column instance. The method further includes performing correlation of the row and column instances. The method also includes building a normalizing graph using a graph-based manifold regularization that enforces a smooth target function which, in turn, assigns a value on each node of the normalizing graph to obtain a Lapacian matrix. The method additionally includes performing Eigenvalue decomposition on the Lapacian matrix to obtain Eigenvectors. The method further includes providing a canonical co-clustering analysis function by maximizing a coupling between clustering vectors while concurrently enforcing regularization on each clustering vector using the Eigenvectors.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: determining, by a clustering vector generator, from a data matrix having rows and columns, a clustering vector of the rows in the data matrix and a clustering vector of the columns in the data matrix, wherein each row in the clustering vector of the rows is a row instance and each row in the clustering vector of the columns is a column instance; performing, by an instance correlator, correlation of the row and column instances; building, by a normalizing graph builder, a normalizing graph using a graph-based manifold regularization that enforces a smooth target function which, in turn, assigns a value on each node of the normalizing graph to obtain a Lapacian matrix; performing, by an Eigenvalue decomposer, Eigenvalue decomposition on the Lapacian matrix to obtain Eigenvectors therefrom; and providing, by a canonical co-clustering analysis function generator, a canonical co-clustering analysis function by maximizing a coupling between the clustering vectors while concurrently enforcing regularization on each of the clustering vectors using the Eigenvectors. 2. The method of claim 1, wherein the dimensions of the rows and the columns of the data matrix are different, and a dimension of the clustering vectors is different from the dimensions of the rows and the columns of the data matrix. 3. The method of claim 1, wherein the normalizing graph is built as a Bipartite graph. 4. The method of claim 3, wherein the canonical co-clustering analysis function is configured as a spectral canonical co-clustering analysis function. 5. The method of claim 1, wherein the normalizing graph is built as a two-component graph having two disconnected components corresponding to two sub-graphs associated with the rows and the columns of the data matrix. 6. The method of claim 5, wherein edge weights of intra-view edges in the two-component graph are determined based on at least one of row similarities and column similarities in at least one similarity matrix determined from the data matrix. 7. The method of claim 5, wherein the edge weights are determined using a similarity function that uses nearest neighbors or a Gaussian function. 8. The method of claim 1, wherein the normalizing graph is built using sub-space clustering. 9. The method of claim 1, wherein the normalizing graph is built to include one or more grouping constraints. 10. The method of claim 1, wherein the normalizing graph is built to include partially labeled samples of the rows and the columns in the data matrix. 11. The method of claim 1, wherein the normalizing graph is built to enforce specific requirements on the canonical co-clustering analysis. 12. A non-transitory article of manufacture tangibly embodying a computer readable program which when executed causes a computer to perform the steps of claim 1. 13. A system, comprising: a clustering vector generator for determining, from a data matrix having rows and columns, a clustering vector of the rows in the data matrix and a clustering vector of the columns in the data matrix, wherein each row in the clustering vector of the rows is a row instance and each row in the clustering vector of the columns is a column instance; an instance correlator for performing correlation of the row and column instances; a normalizing graph builder for building a normalizing graph using a graph-based manifold regularization that enforces a smooth target function which, in turn, assigns a value on each node of the normalizing graph to obtain a Lapacian matrix; an Eigenvalue decomposer for performing Eigenvalue decomposition on the Lapacian matrix to obtain Eigenvectors therefrom; and a canonical co-clustering analysis function generator for providing a canonical co-clustering analysis function by maximizing a coupling between the clustering vectors while concurrently enforcing regularization on each of the clustering vectors using the Eigenvectors. 14. The system of claim 13, wherein the normalizing graph is built as a Bipartite graph. 15. The system of claim 13, wherein the normalizing graph is built as a two-component graph having two disconnected components corresponding to two sub-graphs associated with the rows and the columns of the data matrix. 16. The system of claim 15, wherein edge weights of intra-view edges in the two-component graph are determined based on at least one of row similarities and column similarities in at least one similarity matrix determined from the data matrix. 17. The system of claim 13, wherein the normalizing graph is built using sub-space clustering. 18. The system of claim 13, wherein the normalizing graph is built to include one or more grouping constraints. 19. The system of claim 13, wherein the normalizing graph is built to include partially labeled samples of the rows and the columns in the data matrix. 20. The system of claim 13, wherein the normalizing graph is built to enforce specific requirements on the canonical co-clustering analysis.