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I have 300 data samples with around 4000 dimension feature each. Each input has a 5 dim. output which is in the range of -2 to 2. I am trying to fit a lasso model to it. I went through a few posts which talk about cross validation strategies like this one: Leave one out cross validation algorithm in matlab But I saw that lasso does not support leaveout in Matlab! http://www.mathworks.com/help/stats/lasso.html How can I train a model using leave one out cross validation and fit a model using lasso on my dataset? I am trying to do this in matlab. I would like to get a set of weights which I will be able to use for future predictions on other data. I tried using glmnet: http://www.stanford.edu/~hastie/glmnet_matlab/intro.html but I couldn't compile it on my machine due to lack of proper mex compiler. Any solutions to my problem? Thanks :) |
