Interpretable Market Segmentation on High Dimension Data

Interpretable dimension reduction for classifying functional data

Classification problems involving a categorical class label Y and a functional predictor X(t) are becoming increasingly common. Since X(t) is infinite dimensional, some form of dimension reduction is essential in these problems. Conventional dimension reduction techniques for functional data usually suffer from one or both of the following problems. First, they do not take the categorical respo...

Interpretable Dimension Redu

a study on insurer solvency by panel data model: the case of iranian insurance market

the aim of this thesis is an approach for assessing insurer’s solvency for iranian insurance companies. we use of economic data with both time series and cross-sectional variation, thus by using the panel data model will survey the insurer solvency.

Classification based on dimension transposition for high dimension data

Based on Jordan Curve Theorem, a universal classification method called hyper surface classification (HSC) has recently been proposed. Experimental results are exciting, which show that in three-dimensional space, this method works fairly well in both accuracy and efficiency even for large size data up to 107. However, designing a number of new classifiers is needed with the growing of feature ...

Scalable and Interpretable Data Representation for High-Dimensional, Complex Data

The majority of machine learning research has been focused on building models and inference techniques with sound mathematical properties and cutting edge performance. Little attention has been devoted to the development of data representation that can be used to improve a user’s ability to interpret the data and machine learning models to solve real-world problems. In this paper, we quantitati...