Abstract: Systems and methods for using multi-view learning to leverage highly informative, high-cost, psychophysiological data collected in a laboratory setting, to improve post-traumatic stress disorder (PTSD) screening in the field, where only less-informative, low-cost, speech data are available. Partial least squares (PLS) methods can be used to learn a bilinear factor model, which projects speech and EEG responses onto latent spaces, where the covariance between the projections is maximized. The systems and methods use a speech representation based on a combination of audio and language descriptors extracted from spoken responses to open-ended questions.

Abstract: A method for feature transformation of a data set includes: receiving a data set including original feature samples with corresponding class labels; splitting the data set into a direction optimization set and a training set; using the direction optimization set to calculate an optimum transformation vector that maximizes inter-class separability and minimizes intra-class variance of the feature samples with respect to corresponding class labels; using the optimum transformation vector to transform the rest of the original feature samples of the data set to new feature samples with enhanced discriminative characteristics; and training a classifier using the new feature samples, wherein the method is performed by one or more processors.

Abstract: Systems and methods for using multi-view learning to leverage highly informative, high-cost, psychophysiological data collected in a laboratory setting, to improve post-traumatic stress disorder (PTSD) screening in the field, where only less-informative, low-cost, speech data are available. Partial least squares (PLS) methods can be used to learn a bilinear factor model, which projects speech and EEG responses onto latent spaces, where the covariance between the projections is maximized. The systems and methods use a speech representation based on a combination of audio and language descriptors extracted from spoken responses to open-ended questions.

Abstract: A method for feature transformation of a data set includes: receiving a data set including original feature samples with corresponding class labels; splitting the data set into a direction optimization set and a training set; using the direction optimization set to calculate an optimum transformation vector that maximizes inter-class separability and minimizes intra-class variance of the feature samples with respect to corresponding class labels; using the optimum transformation vector to transform the rest of the original feature samples of the data set to new feature samples with enhanced discriminative characteristics; and training a classifier using the new feature samples, wherein the method is performed by one or more processors.