I need to classify participants in an NLP study into 3 classes, based on multiple sentences spoken by the participant. I performed a feature extraction on each sentence, and so I am left with a matrix of length (# of sentences spoken x feature vector length for each sentence) for each participant. So, for me, each sample is represented by a matrix of varying length, since some participants spoke more sentences than others. What are some ways for me to reduce the dimensionality of each matrix, and also standardize the length, so I can perform an SVM with each participant as a sample?
I am also interested in learning about other methods to classify my samples, if SVMs are not the best fit. Thank you.