How to determine input and target data for classification in neural network
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neamah al-naffakh on 6 Feb 2017
I'm working on gait recognition problem, the aim of this study is to be used for user authentication (i.e. authenticate the authorised user of a mobile device and reject the imposters)
I have data of 36 users
I've successfully extracted features which are (36 rows and 143 columns) for each user and store it in a matrix called All_Feat.
(by the way, column represents the number of the extracted features and row represents the number of samples for each feature).
Then, for each user, I divided the matrix (All_Feat) into two matrixes ( Training matrix, and Test matrix ).
The training matrix contains ( 25 rows and 143 columns) while the testing matrix has (11 rows and 143 columns).
In order to differentiate between User1 and the remaining 35 users, User 2 and remaining 35 users, and so on ..... , I'd like to use Neural Network.
Based on what I have read, training Neural Network requires two classes, the first class contains all the training data of genuine user (e.g. User1) and labelled with 1 , while the second class has the training data of imposters labelled as 0 (which is binary classification, 1 for the authorised user and 0 for imposters).
now my question is:
1- i dont know how to create these classes!
2- For example, if I want to train Neural Network for User1, I have these variables, input and target. what should I assign to these variables? should input= Training matrix of User1? Target=Training matrix of all the remaining Users (35 Users)?
I really appreciate any help!
Walter Roberson on 6 Feb 2017
input should be the features for all of the samples.
target should be 0 for the samples with the genuine user, and 1 for the samples for imposters.
However, for some kinds of neural network, instead target should be [1 0] for the samples for the genuine user, and [0 1] for the samples for imposters.