Multivariate Modality Inference Using Gaussian Kernel
Yansong Cheng,Surajit Ray +1 more
TLDR
An inference framework on the modality of a KDE under multivariate setting using Gaussian kernel is developed and the modal clustering method proposed by [1] for mode hunting is applied.Abstract:
The number of modes
(also known as modality) of a kernel density estimator (KDE) draws lots of
interests and is important in practice. In this paper, we develop an inference
framework on the modality of a KDE under multivariate setting using Gaussian
kernel. We applied the modal clustering method proposed by [1] for mode hunting. A test statistic and its
asymptotic distribution are derived to assess the significance of each mode.
The inference procedure is applied on both simulated and real data sets.read more
Citations
More filters
Journal ArticleDOI
A Review on Modal Clustering
TL;DR: The modal clustering approach as mentioned in this paper draws a correspondence between the groups and the modes of the density function, which can be seen as a more circumscribed problem of estimation, and the number of clusters is also conceptually well defined.
Journal ArticleDOI
A Novel Image Segmentation Combined Color Recognition Algorithm through Boundary Detection and Deep Neural Network
Posted Content
Non-Parametric Cluster Significance Testing with Reference to a Unimodal Null Distribution
Erika S. Helgeson,Eric Bair +1 more
TL;DR: A novel method to evaluate the significance of identified clusters by comparing the explained variation due to the clustering from the original data to that produced by clustering a unimodal reference distribution that preserves the covariance structure in the data is proposed.
References
More filters
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
Bootstrap Methods: Another Look at the Jackknife
TL;DR: In this article, the authors discuss the problem of estimating the sampling distribution of a pre-specified random variable R(X, F) on the basis of the observed data x.
Journal ArticleDOI
Least squares quantization in PCM
TL;DR: In this article, the authors derived necessary conditions for any finite number of quanta and associated quantization intervals of an optimum finite quantization scheme to achieve minimum average quantization noise power.
Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
Least Squares Quantization in PCM
TL;DR: The corresponding result for any finite number of quanta is derived; that is, necessary conditions are found that the quanta and associated quantization intervals of an optimum finite quantization scheme must satisfy.