Described is system for data classification using formal concept analysis (FCA). In a training phase, the system generates a FCA classification lattice, having a structure, using a set of training data. The set of training data comprises training presentations and classifications corresponding to the training presentations. In a classification phase, a set of test data having classes that are hierarchical in nature is classified using the structure of the FCA classification lattice.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for data classification using formal concept analysis (FCA), the system comprising: one or more processors having associated memory with executable instructions encoded thereon such that when executed, the one or more processors perform operations of: using the structure of a formal concept analysis (FCA) classification lattice, generating a classification of a set of input data during a classification phase; generating, in the classification phase, a presentation context vector, mp, from the set of test data, wherein mp is a set of attributes associated with a presentation p in the set of test data; selecting, in the classification phase, a set of voting nodes in the FCA classification lattice; and using, in the classification phase, the set of voting nodes to vote for a classllrcation value for the presentation p. 2. A system for data classification using formal concept analysis (FCA), the system cornprising: one or more processors having associated memory with executable instructions encoded thereon such that when executed, the one or more processors perform operations of: generating, in a training phase, a context table from a set of training data, the context table having rows of object labels and columns of attribute labels; for each training presentation, appending, in the training phase, at least one class column for a classification corresponding to the training presentation to the context table; and generating a FCA classification lattice from the context table. 3. The system as set forth in claim 2, wherein during generation of the FCA classification lattice the at least one class column is treated as a normal attribute, and wherein a sub-structure comprising a plurality of nodes within the FCA classification lattice that is spanned by a given class-attribute is associated with the corresponding classification. 4. (canceled) 5. The system as set forth in claim 1, wherein the set of voting nodes is selected according to a selection function operating on at least mp and the FCA classification lattice. 6. The system as set forth in claim 5, wherein a classification value, c, is voted on according to a voting function operating on at least the output of the selection function, the FCA classification lattice, and mp. 7. The system as set forth in claim 6, wherein the voting function returns a sum of an associated class value of each of the set of voting nodes. 8. The system as set forth in claim 7, wherein each associated class value is weighted by a number of attributes that ft shares with the presentation p. 9. The system as set forth in claim 6, wherein each voting node has an extent comprising a set of objects, and wherein the voting function returns a sum of an associated class value of each voting node, wherein the sum is normalized by a number of objects within the voting node's extent, wherein the normalized sums across all voting nodes are then summed. 10. The system as set forth in claim 9, wherein each voting node has an intent comprising a set of attributes, and wherein the associated class value for each voting node is weighted by a number of attributes in its intent. 11. The system as set forth in claim 2, wherein the set of training data includes objects having attributes, and the FCA classification lattice is generated by treating the plurality of classifications as attributes of objects in the training data. 12. The system as set forth in claim 1, wherein, the set of input data is acquired using at least one of an fMRI sensor, an image sensor, and a sound sensor, and wherein the classification is performed for purposes of at least one of object recognition, image recognition, and sound recognition. 13. A computer-implemented method for data classification using formal concept analysis (FCA), the computer-implemented method using one or more processors to perform operations of: using the structure of a FCA classification lattice, generating classification of a set of input data during a classification phase; generating, in the classification phase, a presentation context vector, mp, from the set of test data, wherein mp is a set of attributes associated with a presentation p in the set of test data; selecting, in the classification phase, a set of votjng nodes in the FCA classificatlon lattice; and using, in the classification phase, the set of voting nodes to vote for a classification value for the presentation p. 14. A computer-implemented method for data classification using formal concept analysis (FCA), the computer-implemented method using one or more processors to perform operations of: generating, in a training phase, a context table from a set of training data, the context table having rows of object labels and columns of attribute labels; for each training presentation, appending, in the training phase, at least one class column for a classification corresponding to the training presentation to the context table; and generating a FCA classification lattice from the context table. 15. The method as set forth in claim 14, wherein during generation of the FCA classification lattice the at least one class column is treated as a normal attribute, and wherein a sub-structure comprising a plurality of nodes within the FCA classification lattice that is spanned by a given class-attribute is associated with the corresponding classification. 16. canceled 17. A computer program product for data classification using formal concept analysis (FCA), the computer program product comprising: computer-readable instructions stored on a non-transitory computer-readable medium that are executable by a computer having one or more processors for causing the one or more processors to perform operations of: using the structure of a FCA classification lattice, generating a classification of a set of input data during a classification phase; generating, in the classification phase, a presentation context vector, mp, torn the set of test data, wherein mp is a set of attributes associated with a presentation p in the set of test data; selecting, in the classification phase, a set of voting nodes in the FCA classification lattice; and using, in the classification phase, the set of voting nodes to vote for a classification value for the presentation p. 18. A computer program product for data classification using formal concept analysis (FCA), the computer program product comprising: computer-readable instructions stored on a non-transitory computer-readable medium that are executable by a computer having one or more processors for causing the one or more processors to perform operations of: generating, in a training phase, a context table from a set of training data, the context table having rows of object labels and columns of attribute labels; for each training presentation, appending, in the training phase, at least one class column for a classification corresponding to the training presentation to the context table; and generating the FCA classification lattice from the context table. 19. The computer program product as set forth in claim 18, wherein during generation of the FCA classification lattice the at least one class column is treated as a normal attribute, and wherein a sub-structure comprising a plurality of nodes within the FCA classification lattice that is spanned by a given class-attribute is associated with the corresponding classification. 20. (canceled) 21. A system for data classification using formal concept analysis (FCA), the system comprising: one or more processors having associated memory with executable instructions encoded thereon such that when executed, the one or more processors perform operations of: generating with the one or more processors, in a training phase, a formal concept analysis (FCA) classification lattice using a set of training data and a plurality of classifications corresponding to the set of training data; and using the structure of the FCA classification lattice, generating a classification of a set of input data during a classification phase. generating, in the training phase, a context table from the set of training data, the context table having rows of object labels and columns of attribute labels; for each training presentation, appending, in the training phase, at least one class column for a classification corresponding to the training presentation to the context table; generating the FCA classification lattice from the context table; wherein during generation of the FCA classification lattice the at least one class column is treated as a normal attribute, and wherein a sub-structure comprising a plurality of nodes within the FCA classification lattice that is spanned by a given class-attribute is associated with the corresponding classification; generating, in the classification phase, a presentation context vector, mp, from the set of test data, wherein mp is a set of attributes associated with a presentation p in the set of test data; selecting, in the classification phase, a set of voting nodes in the FCA classification lattice; and using, in the classification phase, the set of voting nodes to vote for a classification value for the presentation p; wherein the set of voting nodes is selected according to a selection function operating on at least mp and the FCA classification lattice; wherein a classification value, c, is voted on according to a voting function operating on at least the output of the selection function, the FCA classification lattice, and mp.