Learning method, learning apparatus and program

A learning method executed by a computer including a memory and a processor, the learning method comprising:

• receiving as input a set of data sets D={D1, . . . , DT} wherein a task set is {1, . . . , T} and a data set including data at least including feature amount vectors representing features of cases of a task t∈{1, . . . , T} is denoted as Dt;
• sampling a task t from the task set {1, . . . , T}, and sampling a first subset from a data set Dt of the task t and a second subset from a set obtained by excluding the first subset from the data set Dt;
• generating a task vector representing a property of a task t corresponding to the first subset by a first neural network;
• nonlinearly transforming feature amount vectors included in data included in the second subset by a second neural network using the task vector;
• calculating scores representing respective degrees of anomaly of the feature amount vectors using the nonlinearly transformed feature amount vectors and a preset center vector;
• and
• learning a parameter of the first neural network and a parameter of the second neural network so as to make an index value representing generalized performance of anomaly detection higher using the scores.