Abstract: Disclosed herein is an information processing apparatus, comprising: a feature extraction unit configured to extract features from a sample of a first class and a sample of a second class contained in a source domain and a sample of the first class contained in a target domain, respectively; a pseudo-sample generation unit configured to generate pseudo-samples of the second class in the target domain based on a distribution of samples of the first class contained in the target domain in a feature space of the features extracted by the feature extraction unit; and a data transformation unit configured to perform data transformation in the feature space by machine learning such that a distribution of samples of the first class and samples of the second class contained in the source domain approximates a distribution of samples of the first class and the pseudo-samples of the second class in the target domain.

Abstract: Disclosed herein is an information processing apparatus, comprising: a feature extraction unit configured to extract features from a sample of a first class and a sample of a second class contained in a source domain and a sample of the first class contained in a target domain, respectively; a pseudo-sample generation unit configured to generate pseudo-samples of the second class in the target domain based on a distribution of samples of the first class contained in the target domain in a feature space of the features extracted by the feature extraction unit; and a data transformation unit configured to perform data transformation in the feature space by machine learning such that a distribution of samples of the first class and samples of the second class contained in the source domain approximates a distribution of samples of the first class and the pseudo-samples of the second class in the target domain.