Mean shift: An information theoretic perspective

Rao, S.S.; Martins, Allan de Medeiros; Prı́ncipe, José C.

doi:10.1016/j.patrec.2008.09.011

Cited by 46 publications

(36 citation statements)

References 13 publications

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“…The calculation of the IP was performed per Equation (7). From there, it became necessary to use some technique capable of finding the existing dynamics in the data.…”

Section: Information Dynamicsmentioning

confidence: 99%

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An Analysis of Information Dynamic Behavior Using Autoregressive Models

Oliveira

Neto

Martins

2017

Entropy

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Information Theory is a branch of mathematics, more specifically probability theory, that studies information quantification. Recently, several researches have been successful with the use of Information Theoretic Learning (ITL) as a new technique of unsupervised learning. In these works, information measures are used as criterion of optimality in learning. In this article, we will analyze a still unexplored aspect of these information measures, their dynamic behavior. Autoregressive models (linear and non-linear) will be used to represent the dynamics in information measures. As a source of dynamic information, videos with different characteristics like fading, monotonous sequences, etc., will be used.

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“…The calculation of the IP was performed per Equation (7). From there, it became necessary to use some technique capable of finding the existing dynamics in the data.…”

Section: Information Dynamicsmentioning

confidence: 99%

“…Main researches in ITL include regression, neural network training, classification and clustering [3][4][5][6][7][8][9][10], parameter estimation, system identification and signal processing [11][12][13][14][15][16][17] areas.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Information Dynamic Behavior Using Autoregressive Models

Oliveira

Neto

Martins

2017

Entropy

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“…In [31] it was shown that the choice of Gaussian kernels is connected with minimization of Renyi's entropy (see Section 3.4). The intuition that mean shift is a form of gradient ascent, which was pointed out in [12], also makes Gaussian kernels attractive since it allows the mean shift algorithm to be defined from a gradient perspective.…”

Section: Mean Shift With Gaussian Kernelsmentioning

confidence: 99%

Mean Shift Spectral Clustering using Kernel Entropy Component Analysis

Agersborg

Jenssen

2014

2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

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Clustering is an unsupervised pattern recognition technique for finding natural groups in data, whether it is a grouping of web pages found by a search engine or segmenting satellite images into different types of ground cover. There exists a variety of different ways to perform clustering ranging from heuristics rules designed for a specific dataset to general procedures which can be applied to all datasets with varying degrees of success. The k-means algorithm is a well known example of the latter approach that can be expected to give good results when the data is easily separable.Another example of general clustering procedures are spectral clustering methods which involve a non-linear data transformation that allows them to handle complex cluster structures. They are considered to be the state of the art in the clustering literature, but are computationally demanding and unable to handle large datasets. To overcome the size limitations, this thesis uses a two-stage approach. The first stage can be seen as a preprocessing to reduce the size of the input for the spectral clustering in the second stage.The preprocessing is accomplished by using a simple clustering method on the dataset. These clusters represents a partitioning and is a more natural way of representing the dataset than randomly selecting a subset of samples. One can then adjust the number of partitions so spectral clustering methods can be used.The procedure is called Mean Shift Spectral Clustering (MSSC) as the mean shift clustering algorithm is used in the first stage. Each partition found by mean shift consists of data points that are close to the same mode in the estimated probability density function. Hence the partitions will be representative of the geometric structure of the dataset.This thesis realizes for the first time the idea of spectral clustering based on the partitions found by mean shift. The Kernel Entropy Component Analysis (KECA) spectral clustering method, a recent development in the field of spectral clustering, is used for this purpose and compared with the better known Kernel Principal Component Analysis (KPCA) method.A comprehensive collection of MATLAB functions has been developed to allow the testing of this procedure which is able to handle large and high dimensional datasets. It is able to cluster images directly with as many feature vectors as there are pixels. The experiments show how the clustering accuracy varies as a function of the primary parameters. This gives a good overall characterization of the method and what can be expected when used for unfamiliar datasets.The MSSC procedure is shown to provide good clustering results when following some basic principles for selecting parameters.ii

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“…In the following years, the mean shift algorithm met with growing interest in the computer vision community with applications ranging from image segmentation, appearance-based clustering to object recognition and tracking. In recent years, many works have contributed to improve the original mean shift approach [11] [12] [14]. A variable bandwidth mean shift algorithm is proposed in [13].…”

Section: Variable Bandwidth Mean Shiftmentioning

confidence: 99%

A Centroid Based Text Categorization Method Using Mean Shift

Yuan

2013

J. Inf. Comput. Sci.

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Text categorization is an important research topic in Information Retrieval area and it is one of the key techniques for handling and organizing the huge amount of text data available on the Internet and other digital format in our daily life. In this paper, we propose a method for text categorization based on Mean Shift. Mean Shift algorithm is a well developed technique in computer vision researches. We extend the application of Mean Shift to text categorization by reducing the dimensions of text vector space to a proper scale. Firstly, a low-dimensional feature space is constructed using the feature selection method by the theory of information gain. Secondly, an adaptive Mean Shift algorithm is applied for detecting the centers (centroids) of each category on the feature space above. Finally, each document will be added to its most similar category by calculating the similarities between the document and the center of every category. Experimental results on 20NewsGroup and Rueters-21578 corpus show that this method can achieve higher performance than some classic text categorization method like KNN, Naïve Bayes and SVM.

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Mean shift: An information theoretic perspective

Cited by 46 publications

References 13 publications

An Analysis of Information Dynamic Behavior Using Autoregressive Models

An Analysis of Information Dynamic Behavior Using Autoregressive Models

Mean Shift Spectral Clustering using Kernel Entropy Component Analysis

A Centroid Based Text Categorization Method Using Mean Shift

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