Shengdong Zhang
Published 2018-04-15Version 1
CDF2PDF is a method of PDF estimation by approximating CDF. The original idea of it was previously proposed in [1] called SIC. However, SIC requires additional hyper-parameter tunning, and no algorithms for computing higher order derivative from a trained NN are provided in [1]. CDF2PDF improves SIC by avoiding the time-consuming hyper-parameter tuning part and enabling higher order derivative computation to be done in polynomial time. Experiments of this method for one-dimensional data shows promising results.
Unsupervised Discovery of Sparse Multimodal Representations in High Dimensional Data
Anomaly Detection in High Dimensional Data
Diagonal Discriminant Analysis with Feature Selection for High Dimensional Data
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