Method and Device for Classifying Data
A method of classifying data includes: training a classification model for classifying input data into at least one class, such that a first output value is generated according to a second equation in which a component corresponding to a label distribution of source data is disentangled in a first equation corresponding to the classification model; generating a second output value by applying, to the first output value, information indicating a label distribution of target data; and classifying the target data into the at least one class by using the second output value.
1 . A method of classifying data, the method comprising:
training a classification model for classifying input data into at least one class, such that a first output value is generated according to a second equation in which a component corresponding to a label distribution of source data is disentangled in a first equation corresponding to the classification model;
generating a second output value by applying, to the first output value, information indicating a label distribution of target data; and
classifying the target data into the at least one class by using the second output value.
2 . The method of claim 1 , wherein the first equation comprises an equation corresponding to a Bayes' rule representing a probability of the input data being classified as each of the at least one class.
3 . The method of claim 1 , wherein, in the generating of the second output value, the information indicating the label distribution of the target data is applied to the first output value by performing a multiplication operation.
4 . The method of claim 1 , wherein, in the training of the classification model, the classification model is trained by using at least one approximation formula with respect to the second equation and information indicating the label distribution of the source data.
5 . The method of claim 4 , wherein the at least one approximation formula comprises at least one selected from the group consisting of a regularized Donsker-Varadhan (DV) representation and a Monte Carlo approximation formula.
6 . The method of claim 1 , wherein, in the training of the classification model, the classification model is trained by using information indicating regularization with respect to the label distribution of the source data.
7 . The method of claim 1 , wherein training the classification model such that a first output value is generated according to a second equation in which a component corresponding to a label distribution of source data is disentangled in a first equation corresponding to the classification model comprises training the classification model using only the distribution of the samples x (p s (x)) from the training data and the conditional distribution of samples x given the labels y(p s (x|y)).
8 . A computer-readable recording medium having recorded thereon a program for executing the method of claim 1 on a computer.
9 . A device for classifying data, the device comprising:
a memory storing at least one program; and
a processor configured to execute the at least one program to
train a classification model for classifying input data into at least one class, such that a first output value is generated according to a second equation in which a component corresponding to a label distribution of source data is disentangled in a first equation corresponding to the classification model,
generate a second output value by applying, to the first output value, information indicating a label distribution of target data, and
classify the target data into the at least one class by using the second output value.
10 . The device of claim 9 , wherein
the first equation comprises an equation corresponding to a Bayes' rule representing a probability of the input data being classified as each of the at least one class.
11 . The device of claim 9 , wherein
the processor is further configured to execute the at least one program to apply, to the first output value, the information indicating the label distribution of the target data by performing a multiplication operation.
12 . The device of claim 9 , wherein
the processor is further configured to execute the at least one program to train the classification model by using at least one approximation formula with respect to the second equation and information indicating the label distribution of the source data.
13 . The device of claim 12 , wherein
the at least one approximation formula comprises a regularized Donsker-Varadhan (DV) representation and a Monte Carlo approximation formula.
14 . The device of claim 9 , wherein
the processor is further configured to execute the at least one program to train the classification model by using information indicating regularization with respect to the label distribution of the source data.
15 . The device of claim 9 , wherein the processor is further configured to execute the at least one program to train the classification model such that a first output value is generated according to a second equation in which a component corresponding to a label distribution of source data is disentangled in a first equation corresponding to the classification model by training the classification model using only the distribution of the samples x (p s (x)) from the training data and the conditional distribution of samples x given the labels y (p s (x|y)).