Hi , I have been trying to apply an Anomaly Detection model using Spark MLib. I am using this library
org.apache.spark.mllib.stat.distribution.MultivariateGaussian As an input, I give the model a mean vector and a Covariance matrix, assuming my features have Covariance , hence the covariane matrix has all non zero elements. The model returns zero for each data point. While using a model with out covariance, meaning the matrix has zero in all element except the diagonal. The model is working. Now, there is a little documentation for this model. Does anyone have experience applying this model ? any reference will be welcome too These are the model input *mu vector - * 1054.8, 1069.8, 1.3 ,1040.1 *cov matrix - * 165496.0 , 167996.0, 11.0 , 163037.0 167996.0, 170631.0, 19.0, 165405.0 11.0, 19.0 , 0.0, 2.0 163037.0, 165405.0 2.0 , 160707.0 *and for non covariance case * 165496.0, 0.0 , 0.0, 0.0 0.0, 170631.0, 0.0, 0.0 0.0 , 0.0 , 0.8, 0.0 0.0 , 0.0, 0.0, 160594.2 Thanks ! Eyal -- *This email and any files transmitted with it are confidential and intended solely for the use of the individual or entity to whom they are addressed. Please note that any disclosure, copying or distribution of the content of this information is strictly forbidden. If you have received this email message in error, please destroy it immediately and notify its sender.*