I will convert the subtitles into vectors and use them as features to classify the movies into different categories based on their ratings.The problem that I am facing is my feature vector is much much larger compared to the number of examples I have. I would like to know what should the size of my tailing data set be to use LDA,PCA,SVM and naive Bayes.Would 10 movies per category be sufficient?
I have data of some movies and their subtitles.I want to classify them based on their ratings
$\begingroup$ As far as I know, SVM should work fine, even if you have small numbers of records per category. But I would try to use more abstract categories. If possible, try to use not more than 10 different categories. $\endgroup$– Franziska W.Dec 25, 2018 at 9:19
$\begingroup$ Can you please tell me why you think svm works fine? $\endgroup$– Harshita VemulaJan 1, 2019 at 8:51
$\begingroup$ "Geometrically, the SVM modeling algorithm works by con- structing a separating hyperplane with the maximal margin. Compared with other standard classifiers, SVM is more accurate on moderately imbalanced data." - Tang, Chawla (2009): SVMs Modeling for Highly Imbalanced Classification. $\endgroup$– Franziska W.Jan 1, 2019 at 16:44