Methods and apparatus for extending a neural network, reducing its dimension and processing input data are provided. The method of extending a neural network involves selecting, with a processor, a node of a neural network, adding a new node in a layer that includes the selected node, and setting connection weights of the new node based on connection weights of the selected node.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of extending a neural network, the method comprising: selecting, with a processor, a node of a neural network; adding a new node in a layer that includes the selected node; and setting connection weights of the new node. 2. The method of claim 1, further comprising: determining whether an extension of the neural network is required. 3. The method of claim 2, wherein the determining comprises: monitoring a performance of the neural network; and determining that the extension of the neural network is required in response to a level of performance improvement of the neural network being less than a predetermined first threshold value. 4. The method of claim 2, wherein the determining comprises: monitoring a performance of the neural network; and terminating training of the neural network in response to a level of performance improvement of the neural network being less than a predetermined first threshold value, and a level of performance improvement of the neural network after a previous extension of the neural network is performed being less than a predetermined second threshold value. 5. The method of claim 1, wherein the selecting comprises selecting at least one node of the neural network based on at least one of activation frequency information and activation entropy information of nodes in the neural network. 6. The method of claim 5, wherein the activation frequency information comprises an activation frequency of each nodes in the neural network within a predetermined time period. 7. The method of claim 5, wherein the activation entropy information comprises a count of transitions of each nodes in the neural network between an active state and an inactive state within a predetermined time period. 8. The method of claim 5, wherein a node having a highest activation frequency within a predetermined time period is selected among the nodes in the neural network. 9. The method of claim 5, wherein a node having an activation entropy less than a predetermined third threshold value and an activation frequency greater than a predetermined fourth threshold value within a predetermined time period is selected among the nodes in the neural network. 10. The method of claim 1, wherein the adding comprises: connecting the new node to nodes included in a layer preceding the layer; and connecting the new node to nodes included in a layer subsequent to the layer. 11. The method of claim 10, wherein the setting comprises: setting the connection weights of the new node to a predetermined initial value. 12. The method of claim 1, wherein the layer including the selected node is a hidden layer of the neural network. 13. The method of claim 1, wherein the setting comprises: selecting at least a portion of connection weights of the selected node; and setting connection weights of at least a portion of the new node to correspond to the selected portion of the connection weights of the selected node. 14. The method of claim 13, wherein the setting further comprises: setting the selected at least a portion of the connection weights of the selected node to a predetermined initial value. 15. The method of claim 13, wherein the selecting of the at least a portion of the connection weights of the selected node comprises: dividing the connection weights of the selected node into a first group and a second group; and selecting connection weights belonging to one of the first group and the second group. 16. The method of claim 15, wherein the connection weights of the selected node are divided into the first group and the second group so that a difference between a sum of connection weights belonging to the first group and a sum of connection weights belonging to the second group is less than or equal to a predetermined fifth threshold value. 17. The method of claim 1, wherein the setting comprises: setting input connection weights of the new node based on input connection weights of the selected node; selecting a portion of output connection weights of the selected node; setting connection weights corresponding to the selected portion of the output connection weights, among the connection weights of the new node, based on the selected portion of the output connection weights; and setting the selected portion of the output connection weights of the selected node to a predetermined initial value. 18. The method of claim 1, further comprising: setting an activation characteristic of the new node based on an activation characteristic of the selected node. 19. The method of claim 1, further comprising: training the neural network extended to include the new node. 20. The method of claim 1, wherein an initial structure of the neural network comprises a plurality of hidden layers comprising nodes fewer than or equal to a predetermined number. 21. The method of claim 20, wherein the selecting comprises selecting at least one node of the plurality of hidden layers. 22. A method of dimension reduction, the method comprising: training a neural network comprising an input layer, one or more hidden layer, and an output layer; determining whether an extension of a hidden layer is required during the training of the neural network; and extending the hidden layer in response to a determination that the extension of the hidden layer is required. 23. The method of claim 22, further comprising initializing the neural network prior to the training of the neural network, the initializing comprising: generating a plurality of nodes corresponding to the input dimension in the input layer; generating at least one node corresponding to a predetermined initial value in the one or more hidden layer; and generating a plurality of nodes corresponding to the input dimension in the output layer. 24. The method of claim 22, wherein the determining comprises: monitoring a performance of the neural network based on differences between values input into the input layer and values output from the output layer; and determining that an extension of a structure of the neural network is required in response to a level of performance improvement of the neural network being less than a predetermined threshold value. 25. The method of claim 22, wherein the extending comprises: selecting a node of a hidden layer; adding a new node in the hidden layer including the selected node; and setting connection weights of the new node based on connection weights of the selected node. 26. The method of claim 25, wherein the selecting comprises selecting the node based on at least one of activation frequency information and activation entropy information of nodes of the neural network. 27. The method of claim 25, wherein the adding comprises: connecting the new node to nodes included in a layer preceding the hidden layer including the selected node; and connecting the new node to nodes included in layer subsequent to the hidden layer including the selected node. 28. The method of claim 25, wherein the setting comprises: selecting at least a portion of the connection weights of the selected node; and setting a portion of connection weights of the new node to correspond to the selected portion of the connection weights of the selected node. 29. The method of claim 28, wherein the setting further comprises: setting the selected at least a portion of the connection weights of the selected node to a predetermined initial value. 30. The method of claim 22, further comprising: training the neural network comprising the extended hidden layer. 31. A non-transitory computer-readable storage medium storing instructions to cause a computer to perform the method of claim 1. 32. An apparatus for extending a neural network, the apparatus comprising: a memory configured to store a neural network; and a processor configured to extend the neural network by selecting a node of the neural network based on activation information, and adding a new node to a layer that includes the selected node. 33. The apparatus of claim 32, wherein the processor is configured to set connection weights of the new node based on connection weights of the selected node. 34. The apparatus of claim 32, wherein the processor is configured to adjust the connection weights of the new node by training the extended neural network with training data. 35. The apparatus of claim 32, wherein the processor is configured to apply training data from a training data memory storage to the extended neural network and determine whether an extension of a hidden layer is required based on an activation pattern of the extended neural network, and to increase a number of nodes in the hidden layer of the neural network based on the determination that the extension of the hidden layer is required. 36. The apparatus of claim 32, further comprising an input device, wherein the processor is configured to apply input data obtained with use of the input device to the neural network to obtain an output data. 37. The apparatus of claim 32, wherein the layer including the selected node is a hidden layer of the neural network.