73 citations · 221 across the 15 of their papers we have counts for
15 papers
Visualizing the Finer Cluster Structure of Large-Scale and High-Dimensional Data
Yu Liang, Arin Chaudhuri, Haoyu Wang
Dimension reduction and visualization of high-dimensional data have become very important research topics because of the rapid growth of large databases in data science. In this pa…
Fault Detection Using Nonlinear Low-Dimensional Representation of Sensor Data
Kai Shen, Anya Mcguirk, Yuwei Liao +2
Sensor data analysis plays a key role in health assessment of critical equipment. Such data are multivariate and exhibit nonlinear relationships. This paper describes how one can e…
Automatic Hyperparameter Tuning Method for Local Outlier Factor, with Applications to Anomaly Detection
Zekun Xu, Deovrat Kakde, Arin Chaudhuri
In recent years, there have been many practical applications of anomaly detection such as in predictive maintenance, detection of credit fraud, network intrusion, and system failur…
A fast algorithm for computing distance correlation
Arin Chaudhuri, Wenhao Hu
Classical dependence measures such as Pearson correlation, Spearman's , and Kendall's can detect only monotonic or linear dependence. To overcome these limitations, Szekely…
A New SVDD-Based Multivariate Non-parametric Process Capability Index
Deovrat Kakde, Arin Chaudhuri, Diana Shaw
Process capability index (PCI) is a commonly used statistic to measure ability of a process to operate within the given specifications or to produce products which meet the require…
The Trace Criterion for Kernel Bandwidth Selection for Support Vector Data Description
Arin Chaudhuri, Carol Sadek, Deovrat Kakde +6
Support vector data description (SVDD) is a popular anomaly detection technique. The SVDD classifier partitions the whole data space into an inlier region, which consists of the re…
A new bandwidth selection criterion for using SVDD to analyze hyperspectral data
Yuwei Liao, Deovrat Kakde, Arin Chaudhuri +3
This paper presents a method for hyperspectral image classification that uses support vector data description (SVDD) with the Gaussian kernel function. SVDD has been a popular mach…
Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel
Hansi Jiang, Haoyu Wang, Wenhao Hu +2
Support vector data description (SVDD) is a machine learning technique that is used for single-class classification and outlier detection. The idea of SVDD is to find a set of supp…
The Mean and Median Criterion for Automatic Kernel Bandwidth Selection for Support Vector Data Description
Arin Chaudhuri, Deovrat Kakde, Carol Sadek +2
Support vector data description (SVDD) is a popular technique for detecting anomalies. The SVDD classifier partitions the whole space into an inlier region, which consists of the r…
Online Robust Principal Component Analysis with Change Point Detection
Wei Xiao, Xiaolin Huang, Jorge Silva +2
Robust PCA methods are typically batch algorithms which requires loading all observations into memory before processing. This makes them inefficient to process big data. In this pa…
Kernel Bandwidth Selection for SVDD: Peak Criterion Approach for Large Data
Sergiy Peredriy, Deovrat Kakde, Arin Chaudhuri
Support Vector Data Description (SVDD) provides a useful approach to construct a description of multivariate data for single-class classification and outlier detection with various…
A Non-Parametric Control Chart For High Frequency Multivariate Data
Deovrat Kakde, Sergriy Peredriy, Arin Chaudhuri +1
Support Vector Data Description (SVDD) is a machine learning technique used for single class classification and outlier detection. SVDD based K-chart was first introduced by Sun an…
Leveraging Unstructured Data to Detect Emerging Reliability Issues
Deovrat Kakde, Arin Chaudhuri
Unstructured data refers to information that does not have a predefined data model or is not organized in a pre-defined manner. Loosely speaking, unstructured data refers to text d…
Sampling Method for Fast Training of Support Vector Data Description
Arin Chaudhuri, Deovrat Kakde, Maria Jahja +4
Support Vector Data Description (SVDD) is a popular outlier detection technique which constructs a flexible description of the input data. SVDD computation time is high for large t…
Peak Criterion for Choosing Gaussian Kernel Bandwidth in Support Vector Data Description
Deovrat Kakde, Arin Chaudhuri, Seunghyun Kong +3
Support Vector Data Description (SVDD) is a machine-learning technique used for single class classification and outlier detection. SVDD formulation with kernel function provides a…