A learning method includes: randomly selecting one or more feature vectors from feature vectors for learning to form a sample set, by a processor; selecting, from the feature vectors for learning, one of feature vectors appended with a label different from a label appended to a feature vector included in the sample set as a reference vector, the selecting being carried out based on a generalized average of distance from a feature vector included in the sample set, by the processor; and learning a hyperplane that divides a feature vector space, the learning being carried out using a pair of one of feature vectors appended with a label different from a label appended to the reference vector, among the feature vectors for learning, and the selected reference vector, by the processor.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A learning method comprising: randomly selecting one or more feature vectors from feature vectors for learning to form a sample set, by a processor; selecting, from the feature vectors for learning, one of feature vectors appended with a label different from a label appended to a feature vector included in the sample set as a reference vector, the selecting being carried out based on a generalized average of distance from a feature vector included in the sample set, by the processor; and learning a hyperplane that divides a feature vector space, the learning being carried out using a pair of one of feature vectors appended with a label different from a label appended to the reference vector, among the feature vectors for learning, and the selected reference vector, by the processor. 2. The learning method according to claim 1, wherein the learning of hyperplane is carried out using a pair of a feature vector included in the sample set and the reference vector, by the processor. 3. The learning method according to claim 1, wherein the learning of hyperplane is carried out using a pair of one of feature vectors for learning, appended with a label different from a label appended to the reference vector and including a shortest distance from the reference vector, and the reference vector, by the processor. 4. The learning method according to claim 1, wherein among the feature vectors for learning, a distance from each feature vector included in the sample set is calculated for a feature vector appended with a label different from a label appended to a feature vector included in the sample set, by the processor, a generalized average with weighting of the calculated distance is calculated, by the processor, and a feature vector including a smallest of the calculated generalized average is selected as a reference vector, by the processor. 5. The learning method according to claim 1, wherein among the feature vectors for learning, a distance from each feature vector included in the sample set is calculated for a feature vector appended with a label different from a label appended to a feature vector included in the sample set, by the processor, a generalized average with weighting of the calculated distance is calculated, by the processor, and a feature vector including the calculated generalized average of a predetermined percentile is selected as a reference vector, by the processor. 6. The learning method according to claim 1, wherein among the feature vectors for learning, a distance from each feature vector included in the sample set is calculated for a feature vector not included in the sample set, by the processor, an arithmetic average with weighting of the calculated distance is calculated in which plus and minus of the calculated distance between feature vectors appended with a same label is inverted, by the processor, and a feature vector including a smallest of the calculated generalized average is determined as a reference vector, by the processor. 7. The learning method according to claim 1, wherein among the feature vectors for learning, a distance from each feature vector included in the sample set is calculated for a feature vector not included in the sample set, by the processor, an arithmetic average with weighting of the calculated distance is calculated in which plus and minus of the calculated distance between feature vectors appended with a same label is inverted, by the processor, and a feature vector including the calculated generalized average of a predetermined percentile is determined as a reference vector, by the processor. 8. An information processing device comprising: a processor configured to execute a process including: randomly selecting one or more feature vectors from feature vectors for learning to form a sample set; selecting, from the feature vectors for learning, one of feature vectors appended with a label different from a label appended to a feature vector included in the sample set selected at the randomly selecting as a reference vector, the selecting being carried out based on a generalized average of distance from a feature vector included in the sample set; and learning a hyperplane that divides a feature vector space, the learning being carried out using a pair of one of feature vectors appended with a label different from a label appended to the reference vector selected at the selecting, among the feature vectors for learning, and the reference vector selected at the selecting. 9. A non-transitory computer-readable recording medium storing a learning program that causes a computer to execute a process comprising: randomly selecting one or more feature vectors from feature vectors for learning to form a sample set; selecting, from the feature vectors for learning, one of feature vectors appended with a label different from a label appended to a feature vector included in the sample set as a reference vector, the selecting being carried out based on a generalized average of distance from a feature vector included in the sample set; and learning a hyperplane using a pair of one of feature vectors appended with a label different from a label appended to the reference vector, among the feature vectors for learning, and the selected reference vector.