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As an effective dimensionality reduction method, Same Degree Distribution (SDD) has been demonstrated to be able to maintain better data structure than other dimensionality reduction methods, including Principal Component Analysis (PCA), ...

With the development of technology and the widespread collection of data, high-dimensional data analysis has become a research hotspot in many fields. Traditional parameter methods often face problems such as dimensional disasters in high-dimensional ...

Interpretability and stability are two important characteristics required for the application of high dimensional data in statistics. Although the former has been favored by many existing forecasting methods to some extent, the latter in the sense ...

Along with the success of deep learning are extensive models and huge amounts of data. Therefore, efficiency in deep learning is one of the most concerning problems. Many methods have been proposed to reduce the complexity of models and have achieved ...

High dimensional data can have a surprising property: pairs of data points may be easily separated from each other, or even from arbitrary subsets, with high probability using just simple linear classifiers. However, this is more of a rule of ...

We investigate the problem of successfully learning from just a few examples of data points in a binary classification problem, and present a brief overview of some recent results on the role of nonlinear feature maps in this challenging task. Our ...

A robust and sparse estimator for multinomial regression is proposed for high dimensional data. Robustness of the estimator is achieved by trimming the observations, and sparsity of the estimator is obtained by the elastic net penalty. In contrast ...

Learning from high dimensional data has been utilized in various applications such as computational biology, image classification, and finance. Most classical machine learning algorithms fail to give accurate predictions in high dimensional ...

• This study adds more external information to predict corporate credit risk (CCR).

Accurate credit risk prediction can help companies avoid bankruptcies and make adjustments ahead of time. There is a tendency in corporate credit risk prediction that more and more features are considered in the prediction system. ...

Ensemble diversity is an important characteristic of Multiple Classifier Systems (MCS), which aim at improving the overall performance of a classification system by combining the response of several models. While diversity may be ...

• Local-based ensemble methods produce and/or exploit models specialized in each area.

This paper describes a correlation-based batch process for addressing high dimensional imputation problems. There are relatively few algorithms designed to efficiently handle imputation of missing data in high dimensional contexts. Fewer still are ...

This paper proposes a new test for a change point in the mean of high-dimensional data based on the spatial sign and self-normalization. The test is easy to implement with no tuning parameters, robust to heavy-tailedness and ...

In recent years, technology has continued to rapidly evolve, resulting in the generation of high-dimensional data streams. Combining the streaming scenario and high dimensionality is a particularly complex task specifically for outlier detection. ...

A useful strategy to perform outlier detection (OD) in highdimensional data, especially in the presence of multiple classes of outliers, is to decompose the outlier detection problem into a set of relevant subspace selection (RSS) ...

• Granulation of data using different encoding mechanisms.
• Design of fuzzy rule-...

This study opens an original avenue of designing interpretable fuzzy rule-based models realized in the presence of data characterized by a large number of attributes (input variables). In the presence of such high-dimensional data, the ...

Mixtures of von Mises-Fisher distributions can be used to cluster data on the unit hypersphere. This is particularly adapted for high-dimensional directional data such as texts. We propose in this article to estimate a von Mises ...

The concept of any method to resolve feature selection issues is to identify a subset of the original features. However, determining a minimal feature subset is considered an NP-hard problem. Many existing feature selection methods, ...

We introduce the Robust Logistic Zero-Sum Regression (RobLZS) estimator, which can be used for a two-class problem with high-dimensional compositional covariates. Since the log-contrast model is employed, the estimator is able to do feature ...

In the paradigm of online active classification, the learner not only has to predict the label of each incoming instance, but also must decide whether the true label of that instance should be supplied, or not. The overall goal is to minimize the ...

Single Cell RNA Sequencing (scRNA-seq) technology has enabled the biological research community to explore gene expression at a single-cell resolution. By studying differences in gene expression, it is possible to differentiate cell clusters and ...