The Split-Apply-Combine Strategy for Data Analysis

Split-and-Combine Singular Value Decomposition for Large-Scale Matrix

The singular value decomposition (SVD) is a fundamental matrix decomposition in linear algebra. It is widely applied in many modern techniques, for example, highdimensional data visualization, dimension reduction, data mining, latent semantic analysis, and so forth. Although the SVD plays an essential role in these fields, its apparent weakness is the order three computational cost. This order ...

Statistical Analysis Methods for the fMRI Data

Functional magnetic resonance imaging (fMRI) is a safe and non-invasive way to assess brain functions by using signal changes associated with brain activity. The technique has become a ubiquitous tool in basic, clinical and cognitive neuroscience. This method can measure little metabolism changes that occur in active part of the brain. We process the fMRI data to be able to find the parts of br...

a time-series analysis of the demand for life insurance in iran

با توجه به تجزیه و تحلیل داده ها ما دریافتیم که سطح درامد و تعداد نمایندگیها باتقاضای بیمه عمر رابطه مستقیم دارند و نرخ بهره و بار تکفل با تقاضای بیمه عمر رابطه عکس دارند

A Randomized Strategy for Learning to Combine Many Features

We consider the problem of learning a predictor by combining possibly infinitely many linear predictors whose weights are to be learned, too, an instance of multiple kernel learning. To control overfitting a group p-norm penalty is used to penalize the empirical loss. We consider a reformulation of the problem that lets us implement a randomized version of the proximal point algorithm. The key ...

MIKE Data Analysis Strategy