An information conversion method includes: first moving positions of a plurality of particles on a unit sphere according to a value of a probability density function, defining a positional vector of a particle on the unit sphere in a multidimensional space, as a normal vector of a hyperplane configured to divide a feature vector space, defining a predetermined evaluation function configured to evaluate the hyperplane, as the probability density function configured to indicate a probability of existence of a particle on the unit sphere, by a processor; and converting the feature vector to a binary string, considering a positional vector of the moved particle as a normal vector of the hyperplane, by the processor.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An information conversion method comprising: first moving positions of a plurality of particles on a unit sphere according to a value of a probability density function, defining a positional vector of a particle on the unit sphere in a multidimensional space, as a normal vector of a hyperplane configured to divide a feature vector space, defining a predetermined evaluation function configured to evaluate the hyperplane, as the probability density function configured to indicate a probability of existence of a particle on the unit sphere, by a processor; and converting the feature vector to a binary string, considering a positional vector of the moved particle as a normal vector of the hyperplane, by the processor. 2. The information conversion method according to claim 1, wherein the first moving includes extracting a movement destination candidate of the particle, by the processor, calculating a value obtained by dividing a value of the evaluation function calculated by defining a positional vector of the particle moved to the movement destination candidate, as a normal vector of the hyperplane, by a value of the evaluation function calculated by defining a current positional vector of the particle, as a normal vector of the hyperplane, by the processor, and second moving the particle to the extracted movement destination candidate, upon obtaining the calculated value larger than a random number of not less than 0 and not more than 1, or preventing movement of the particle, upon obtaining the calculated value not more than the random number, by the processor. 3. The information conversion method according to claim 1, wherein the first moving is repetitively performed a predetermined number of times, by the processor. 4. The information conversion method according to claim 1, wherein, as the probability density function, an evaluation function is used to move the plurality of particles on the unit sphere, the evaluation function selecting a pair of feature vectors to which the same label is applied, as a positive example pair, selecting a pair of feature vectors to which different labels are applied, as a negative example pair, and having a value reduced upon dividing two feature vectors included in the positive example pair into different areas by the hyperplane, and the value increased upon dividing two feature vectors included in the negative example pair into different areas by the hyperplane. 5. The information conversion method according to claim 4, wherein, as the probability density function, an evaluation function is used, the evaluation function selecting a plurality of the positive example pairs and a plurality of the negative example pairs, defining, as an index, the sum of the number of positive example pairs including both feature vectors in one of the areas divided by the hyperplane, of the selected positive example pairs, and the number of negative example pairs including two feature vectors divided into different areas by the hyperplane, of the selected negative example pairs, and defining a Napier's constant as a base. 6. The information conversion method according to claim 4, wherein, as the probability density function, an evaluation function is used, the evaluation function selecting a plurality of the positive example pairs and a plurality of the negative example pairs, and defining, as an index, the sum of a ratio of positive example pairs including both feature vectors in one of the areas divided by the hyperplane, of the selected positive example pairs, and a ratio of negative example pairs including two feature vectors divided into different areas by the hyperplane, of the selected negative example pairs, and defining a Napier's constant as a base. 7. The information conversion method according to claim 4, wherein, as the probability density function, an evaluation function is used, the evaluation function selecting a plurality of the positive example pairs and a plurality of the negative example pairs, adding, to all the positive example pairs, an absolute value obtained by adding a cosine value of an angle between one feature vector included in the positive example pair and a normal vector of the hyperplane, and a cosine value of an angle between the other feature vector the normal vector of the hyperplane, adding, to all the negative example pair, an absolute value of a difference between a cosine value of an angle between one feature vector included in the negative example pair and a normal vector of the hyperplane, and a cosine value of an angle between the other feature vector and the normal vector of the hyperplane and defining, as an index, the sum of a value added to the positive example pair and a value added to the negative example pair, and defining a Napier's constant as a base. 8. The information conversion method according to claim 4, wherein, as the probability density function, an evaluation function is used, the evaluation function selecting a plurality of the positive example pairs and a plurality of the negative example pairs, adding, to all the positive example pairs, an absolute value obtained by adding a cosine value of an angle between one feature vector included in the positive example pair and a normal vector of the hyperplane, and a cosine value of an angle between the other feature vector the normal vector of the hyperplane, adding, to all the negative example pair, an absolute value of a difference between a cosine value of an angle between one feature vector included in the negative example pair and a normal vector of the hyperplane, and a cosine value of an angle between the other feature vector and the normal vector of the hyperplane and defining, as an index, the sum of a value obtained by dividing a value added to the positive example pair by the number of the positive example pairs, and a value obtained by dividing a value added to the negative example pair by a value of the negative example pair, and defining a Napier's constant as a base. 9. An information conversion device comprising: a processor configured to execute a process including: calculating positions to which a plurality of particles on a unit sphere are moved, defining a positional vector of a particle on the unit sphere in a multidimensional space, as a normal vector of a hyperplane configured to divide a feature vector space, considering a predetermined evaluation function configured to evaluate the hyperplane, as the probability density function configured to indicate a probability of existence of a particle on the unit sphere; and converting the feature vector to a binary string, considering a positional vector indicating a position of each particle calculated at the calculating, as the normal vector of the hyperplane. 10. A non-transitory computer-readable recording medium storing an information conversion program that causes a computer to execute a process comprising: first moving positions of a plurality of particles on a unit sphere according to a value of a probability density function, defining a positional vector of a particle on the unit sphere in a multidimensional space, as a normal vector of a hyperplane configured to divide a feature vector space, defining a predetermined evaluation function configured to evaluate the hyperplane, as the probability density function configured to indicate a probability of existence of a particle on the unit sphere; and converting the feature vector to a binary string, considering a positional vector of the moved particle as a normal vector of the hyperplane.