Transductive lasso for high-dimensional data regression problems

An information processing system for selecting features from a feature space, the information processing system comprising:

• a memory;
• a processor communicatively coupled to the memory;
• and
• a feature selection module communicatively coupled to the memory and the processor, wherein the feature selection module programs the processor to perform a method comprising:
• receiving a set of training samples and a set of test samples, wherein each of the set of training samples comprises a set of features and a target value, and wherein the set of test samples comprises the set of features absent the target value;
• determining a first centered Gram matrix of a given dimension for each of a set of feature vectors comprising at least one of the set of training samples and at least one of the set of test samples;
• determining a second centered Gram matrix of the given dimension for a target value vector comprising the target values from the set of training samples;
• and
• selecting a subset of features from the set of features based on the first and second centered Gram matrices.

A computer program product for selecting features from a feature space, the computer program product comprising:

• a non-transitory storage medium readable by a processing circuit and storing instructions for execution by the processing circuit, the instructions programming the processing circuit to perform a method comprising:
• receiving, by a processor, a set of training samples and a set of test samples, wherein each of the set of training samples comprises a set of features and a target value, and wherein the set of test samples comprises the set of features absent the target value;
• determining a first centered Gram matrix of a given dimension for each of a set of feature vectors comprising at least one of the set of training samples and at least one of the set of test samples;
• determining a second centered Gram matrix of the given dimension for a target value vector comprising the target values from the set of training samples;
• selecting a subset of features from the set of features based on the first and second centered Gram matrices.