Navigating a Hierarchical Abstraction of Topics via an Augmented Gamma Belief Network Operation
A method, system and computer readable medium for generating a cognitive insight comprising: receiving data, the data comprising a plurality of examples, each of the plurality of examples comprising an input object and a desired output value, at least some of the plurality of examples being based upon feedback from a user; performing a machine learning operation on the data, the machine learning operation comprising performing an augmented gamma belief network operation, the augmented gamma belief network operation, the data comprising a plurality of components, at least some of the components being undefined prior to initiating the machine learning operation on the data; and, generating a cognitive insight based upon the cognitive profile generated using the inferred function generated by the augmented gamma belief network operation.
1 . A computer implementable method for generating a cognitive insight comprising:
receiving data, the data comprising a plurality of examples, each of the plurality of examples comprising an input object and a desired output value, at least some of the plurality of examples being based upon feedback from a user;
performing a machine learning operation on the data, the machine learning operation comprising performing an augmented gamma belief network operation, the augmented gamma belief network operation, the data comprising a plurality components, at least some of the components being undefined prior to initiating the machine learning operation on the data; and,
generating a cognitive insight based upon the cognitive profile generated using the inferred function generated by the augmented gamma belief network operation.
2 . The method of claim 1 , wherein:
the augmented gamma belief network operation is applied to a plurality of abstraction layers, each of the plurality of abstraction layers comprising a respective plurality of domain topics.
3 . The method of claim 2 , wherein:
each of the respective plurality of topics of each of the plurality of abstraction layers comprise a plurality of associated attributes.
4 . The method of claim 2 , wherein:
each of the plurality of abstraction layers are abstracted into a topic model, where topics that have a higher degree of abstraction are associated with upper levels of the topic model and topics that have a lesser degree of abstraction are associated with lower levels of the topic mode.
5 . The method of claim 2 , wherein:
each of the respective plurality of topics of each of the plurality of abstraction layers comprises an associated topic relevance distribution value; and further comprising:
determining a number of abstraction layers and a number of topics for a particular abstraction layer based upon the associated topic relevance distribution value associated with each topic within the particular abstraction layer.
6 . The method of claim 5 , further comprising:
associating each of the respective plurality of topics for a particular abstraction layer with a plurality of topics of a contiguous abstraction layer via a plurality of topic relevance distribution values.
7 . A system comprising:
a processor;
a data bus coupled to the processor; and
a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for:
receiving data, the data comprising a plurality of examples, each of the plurality of examples comprising an input object and a desired output value, at least some of the plurality of examples being based upon feedback from a user;
performing a machine learning operation on the data, the machine learning operation comprising performing an augmented gamma belief network operation, the augmented gamma belief network operation, the data comprising a plurality of components, at least some of the components being undefined prior to initiating the machine learning operation on the data; and,
generating a cognitive insight based upon the cognitive profile generated using the inferred function generated by the augmented gamma belief network operation.
8 . The system of claim 7 , wherein the instructions executable by the processor further comprise instructions for:
the augmented gamma belief network operation is applied to a plurality of abstraction layers, each of the plurality of abstraction layers comprising a respective plurality of domain topics.
9 . The system of claim 8 , wherein:
each of the respective plurality of topics of each of the plurality of abstraction layers comprise a plurality of associated attributes.
10 . The system of claim 8 , wherein:
each of the plurality of abstraction layers are abstracted into a topic model, where topics that have a higher degree of abstraction are associated with upper levels of the topic model and topics that have a lesser degree of abstraction are associated with lower levels of the topic mode.
11 . The system of claim 8 , wherein:
each of the respective plurality of topics of each of the plurality of abstraction layers comprises an associated topic relevance distribution value; and the instructions are further configured for:
determining a number of abstraction layers and a number of topics for a particular abstraction layer based upon the associated topic relevance distribution value associated with each topic within the particular abstraction layer.
12 . The system of claim 11 , wherein the instructions are further configured for:
associating each of the respective plurality of topics for a particular abstraction layer with a plurality of topics of a contiguous abstraction layer via a plurality of topic relevance distribution values.
13 . A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for:
receiving data, the data comprising a plurality of examples, each of the plurality of examples comprising an input object and a desired output value, at least some of the plurality of examples being based upon feedback from a user;
performing a machine learning operation on the data, the machine learning operation comprising performing an augmented gamma belief network operation, the augmented gamma belief network operation, the data comprising a plurality of components, at least some of the components being undefined prior to initiating the machine learning operation on the data; and,
generating a cognitive insight based upon the cognitive profile generated using the inferred function generated by the augmented gamma belief network operation.
14 . The non-transitory, computer-readable storage medium of claim 13 , wherein the instructions executable by the processor further comprise instructions for:
the augmented gamma belief network operation is applied to a plurality of abstraction layers, each of the plurality of abstraction layers comprising a respective plurality of domain topics.
15 . The non-transitory, computer-readable storage medium of claim 14 , wherein:
each of the respective plurality of topics of each of the plurality of abstraction layers comprise a plurality of associated attributes.
16 . The non-transitory, computer-readable storage medium of claim 14 , wherein:
each of the plurality of abstraction layers are abstracted into a topic model, where topics that have a higher degree of abstraction are associated with upper levels of the topic model and topics that have a lesser degree of abstraction are c associated with lower levels of the topic mode,
17 . The non-transitory, computer-readable storage medium of claim 14 , wherein:
each of the respective plurality of topics of each of the plurality of abstraction layers comprises an associated topic relevance distribution value; and the instructions are further configured for:
determining a number of abstraction layers and a number of topics for a particular abstraction layer based upon the associated topic relevance distribution value associated with each topic within the particular abstraction layer.
18 . The non-transitory, computer-readable storage medium of claim 17 , wherein the instructions are further configured for:
associating each of the respective plurality of topics for a particular abstraction layer with a plurality of topics of a contiguous abstraction layer via a plurality of topic relevance distribution values.
19 . The non-transitory, computer-readable storage medium of claim 13 , wherein the computer executable instructions are deployable to a client system from a server system at a remote location.
20 . The non-transitory, computer-readable storage medium of claim 13 , wherein the computer executable instructions are provided by a service provider to a user on an on-demand basis.