System, method, and program for predicting information

A method for consistently and accurately predicting information performed by an information predicting computer system that includes

• a learning object storage device that stores objects to be learned,
• a learning result storage device that stores learning results, and
• a control section including a processor and connected to an input section,
• the method causes the control section to perform:
• learning processing in which the control section
• computes a first feature vector and a second feature vector for each object to be learned stored in the learning object storage device,
• generates a first normalized row vector for each object to be learned by subtracting an average vector of the first feature vectors from the first feature vectors to produce a set of first values and dividing each first value by an index that represents variation of the first feature vectors,
• generates a second normalized row vector to be coupled with the first normalized row vector for each object to be learned by subtracting an average vector of the second feature vectors from the second feature vectors to produce a set of second values and dividing each second value by an index that represents variation of the second feature vectors, thereby equalizing scales of the variations of the principal component coefficients of the first and second feature vectors prior to principal component analysis,
• uses coupling learning to generate a coupled principal component basis vector after generating the first and second normalized row vectors by:
• generating a coupled row vector for each object to be learned by coupling the first row vector and the second row vector, and then
• performing principal component analysis using the coupled row vectors of all of the objects to be learned, so as to generate a coupled principal component basis vector, and
• stores the coupled principal component basis vector in the learning result storage device, together with an average vector of the coupled row vectors, and
• prediction processing, in which the control section
• computes a first feature vector of an object to be processed that is designated by the input section, and computes a principal component coefficient of the first feature of the object to be processed, and
• using the principal component coefficient of the first feature vector, the average vector of the coupled row vectors that is stored in the learning result storage device, and the coupled principal component basis vector that is stored in the learning result storage device, and computes a second feature vector of the object to be processed.

A non-transitory computer readable storage medium storing a program for consistently and accurately predicting information executed by an information predicting computer system that includes

• a learning object storage device that stores objects to be learned,
• a learning result storage device that stores learning results, and
• a control section including a processor connected to an input section,
• when the program is executed, the control section performs
• learning processing in which the control section
• computes a first feature vector and a second feature vector for each object to be learned stored in the learning object storage device,
• generates a first normalized row vector for each object to be learned by subtracting an average vector of the first feature vectors from the first feature vectors to produce a set of first values and dividing each first value by an index that represents variation of the first feature vectors,
• generates a second normalized row vector to be coupled with the first normalized row vector for each object to be learned by subtracting an average vector of the second feature vectors from the second feature vectors to produce a set of second values and dividing each second value by an index that represents variation of the second feature vectors, thereby equalizing scales of the variations of the principal component coefficients of the first and second feature vectors prior to principal component analysis,
• uses coupling learning to generate a coupled principal component basis vector after generating the first and second normalized row vectors by:
• generating a coupled row vector for each object to be learned by coupling the first normalized row vector and the second normalized row vector, and
• performing principal component analysis using the coupled row vectors of all of the objects to be learned, so as to generate a coupled principal component basis vector, and
• stores the coupled principal component basis vector in the learning result storage device, together with an average vector of the coupled row vectors, and
• prediction processing, in which the control section
• computes a first feature vector of an object to be processed that is designated by the input section, and computes a principal component coefficient of the first feature vector of the object to be processed, and
• using the principal component coefficient of the first feature vector, the average vector of the coupled row vectors that is stored in the learning result storage device, and the coupled principal component basis vector that is stored in the learning result storage device, and computes a second feature vector of the object to be processed.