Data on health status of patients can be high-dimensional (+ have gene expression values for thousands of genes which is “high dimensional” data set. In other hands, It should be high dimensional big data. I want to implement my PPDP algorithm on it and then execute data mining operation like classification. Dorothea n= p= (M, half is artificially added noise) k=2 (~10x unbalanced) From NIPS
The proposed approach handles both tall/narrow and wide/short datasets. We further provide an open source implementation based on. In fact, “high dimension” has a very rigorous meaning: it means a data set whenever p>n, no matter what p is or n is. Because in statistics, you will never have. Much of my research in machine learning is aimed at small-sample, high- dimensional bioinformatics data sets. For instance, here is a paper of.
FFVA is a nearest neighbor search technique that facilitates rapidly indexing and recovering the most similar matches to a high-dimensional database of. Preprocessing of High Dimensional Dataset for Developing Expert IR System. Abstract: Now-a-days due to increase in the availability of computing facilities. The properties of high dimensionality are often poorly understood or . in cancer diagnosis, prognosis and prediction generate high-dimensional data sets.