Ordinal contrastive learning for imputing missing data with progressive labels

A method of training a generative model for missing data imputation, the method comprising:

• acquiring, by an electronic device, a first dataset;
• embedding, by the electronic device using an encoder included in the generative model, each of a plurality of data included in the first dataset into an embedding space;
• reconstructing, by the electronic device using a decoder included in the generative model, each of the plurality of data included in the first dataset based on a result of the embedding;
• and
• training, by the electronic device, the encoder based on the result of the embedding,
• wherein the first dataset comprises ordered data across a plurality of modality types,
• wherein the first dataset is a missing dataset in which a portion of data is missing, and
• wherein training the encoder comprises training the encoder such that the positions of the plurality of data in the embedding space are determined based on their order in the first dataset.