A novel method and/or system of feature selection is described.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of feature selection comprising: generating from a set of signal samples a set of feature signal vectors for a set of features in which a respective signal vector in the set of feature signal vectors corresponds to a respective feature in the set of features; determining the following for the respective feature signal vectors in the set of feature signal vectors: a normalized version of Hilbert-Schmidt Independence between a respective feature signal vector of the set of feature signal vectors and an output signal array, a normalized version of Hilbert-Schmidt Independence between the respective feature signal vector of the set of feature signal vectors and the remaining feature signal vectors of the set of feature signal vectors, and a gradient per feature of the set of features using the previously determined normalized versions of Hilbert-Schmidt Independence for the set of feature signal vectors; and selecting a feature having the feature signal vector of the set of feature signal vectors that produces the largest gradient. 2. The method of claim 1, and further comprising: removing the signal samples from the set of signal samples and the feature signal vector from the set of feature signal vectors associated with the selected feature to produce an updated set of feature signal vectors; determining the following for the respective feature signal vectors in the set of updated feature signal vectors: a normalized version of Hilbert-Schmidt Independence between a respective feature signal vector of the set of updated feature signal vectors and an output signal array, a normalized version of Hilbert-Schmidt Independence between the respective feature signal vector of the set of updated feature signal vectors and the remaining feature signal vectors of the set of updated feature signal vectors, and a gradient per feature of the set of features using the previously determined normalized versions of Hilbert-Schmidt Independence for the set of updated feature signal vectors; selecting a feature having the feature signal vector of the updated set of feature signal vectors that produces the largest gradient; and repeating the removing, determining and selecting until a particular number of features are selected. 3. The method of claim 1, wherein the gradient is computed substantially in accordance with the LARS process. 4. The method of claim 1, wherein the determining comprises using the Nystrom approximation to determine a normalized version of Hilbert-Schmidt Independence between a respective feature signal vector of the set of updated feature signal vectors and an output signal array, and to determine a normalized version of Hilbert-Schmidt Independence between the respective feature signal vector of the set of updated feature signal vectors and the remaining feature signal vectors of the set of updated feature signal vectors. 5. The method of claim 4, wherein the determining the normalized version of Hilbert-Schmidt Independence and the determining the gradient is computed in a distributed computing manner. 6. The method of claim 2, wherein the determining comprises using the Nystrom approximation to determine a normalized version of Hilbert-Schmidt Independence between a respective feature signal vector of the set of updated feature signal vectors and an output signal array, and to determine a normalized version of Hilbert-Schmidt Independence between the respective feature signal vector of the set of updated feature signal vectors and the remaining feature signal vectors of the set of updated feature signal vectors. 7. The method of claim 6, wherein the determining the normalized version of Hilbert-Schmidt Independence and the determining the gradient is computed in a distributed computing manner. 8. The method of claim 7, wherein the method is executed on a Hadoop distributed computing architecture system. 9. The method of 1, wherein the features selected comprise substantially non-redundant features. 10. The method of claim 9, wherein the size of the signal sample set exceeds 20,000 samples. 11. The method of claim 10, wherein the dimensionality of the signal sample set exceeds 1000. 12. The method of claim 11, wherein the number of features selected exceeds 50. 13. The method of claim 11, wherein the signal sample set comprises a dense signal sample set. 14. A system comprising: a computing device; the computing device to generate a set of feature signal vectors for a set of features in which a respective signal vector in the set of feature signal vectors corresponds to a respective feature in the set of features, to determine the following for the respective feature signal vectors in the set of feature signal vectors: a normalized version of Hilbert-Schmidt Independence between a respective feature signal vector of the set of feature signal vectors and an output signal array, a normalized version of Hilbert-Schmidt Independence between the respective feature signal vector of the set of feature signal vectors and the remaining feature signal vectors of the set of feature signal vectors, and a gradient per feature of the set of features to be determined with the previously determined normalized versions of Hilbert-Schmidt Independence for the set of feature signal vectors, and to select a feature having the feature signal vector of the set of feature signal vectors to produce the largest gradient. 15. The system of claim 14, wherein the computing device further to remove the signal samples from the set of signal samples and the feature signal vector from the set of feature signal vectors associated with the selected feature to produce an updated set of feature signal vectors and to further determine the following for the respective feature signal vectors in the set of updated feature signal vectors: a normalized version of Hilbert-Schmidt Independence between a respective feature signal vector of the set of updated feature signal vectors and an output signal array, a normalized version of Hilbert-Schmidt Independence between the respective feature signal vector of the set of updated feature signal vectors and the remaining feature signal vectors of the set of updated feature signal vectors, and a gradient per feature of the set of features to be determined with the previously determined normalized versions of Hilbert-Schmidt Independence for the set of updated feature signal vectors, and to select a feature having the feature signal vector of the updated set of feature signal vectors to produce the largest gradient. 16. The system of claim 15, wherein the computing device further to again remove, determine and select one or more additional times until a particular number of features is reached. 17. The system of claim 14, wherein the system comprises a distributed computing system, wherein the computing device comprises more than one computing device in the distributed computing system, and wherein the more than one computing devices in the distributed computing system are to determine for the respective feature signal vectors in the set of feature signal vectors in a distributed computing fashion. 18. A system comprising: means for generating a set of feature signal vectors for a set of features in which a respective signal vector in the set of feature signal vectors corresponds to a respective feature in the set of features; means for determining the following for the respective feature signal vectors in the set of feature signal vectors: a normalized version of Hilbert-Schmidt Independence between a respective feature signal vector of the set of feature signal vectors and an output signal array, a normalized version of Hilbert-Schmidt Independence between the respective feature signal vector of the set of feature signal vectors and the remaining feature signal vectors of the set of feature signal vectors, and a gradient per feature of the set of features using the previously determined normalized versions of Hilbert-Schmidt Independence for the set of feature signal vectors; and means for selecting a feature having the feature signal vector of the set of feature signal vectors that produces the largest gradient. 19. The system of claim 18, and further comprising: means for removing the signal samples and feature signal vector associated with the selected feature to produce an updated set of feature signal vectors; means for determining the following for the respective feature signal vectors in the set of updated feature signal vectors: a normalized version of Hilbert-Schmidt Independence between a respective feature signal vector of the set of updated feature signal vectors and an output signal array, a normalized version of Hilbert-Schmidt Independence between the respective feature signal vector of the set of updated feature signal vectors and the remaining feature signal vectors of the set of updated feature signal vectors, and a gradient per feature of the set of features using the previously determined normalized versions of Hilbert-Schmidt Independence for the set of updated feature signal vectors; and means for selecting a feature having the feature signal vector of the updated set of feature signal vectors that produces the largest gradient. 20. The system of claim 19, and further comprising means for repeating removing, determining and selecting until a particular number of features are selected.