Search results for "Mahalanobis distance"

LogDet divergence-based metric learning with triplet constraints and its applications.

2014

How to select and weigh features has always been a difficult problem in many image processing and pattern recognition applications. A data-dependent distance measure can address this problem to a certain extent, and therefore an accurate and efficient metric learning becomes necessary. In this paper, we propose a LogDet divergence-based metric learning with triplet constraints (LDMLT) approach, which can learn Mahalanobis distance metric accurately and efficiently. First of all, we demonstrate the good properties of triplet constraints and apply it in LogDet divergence-based metric learning model. Then, to deal with high-dimensional data, we apply a compressed representation method to learn…

A gallery of Chua's Attractors - Part IV

2007

Online Density Estimation of Heterogeneous Data Streams in Higher Dimensions

2016

The joint density of a data stream is suitable for performing data mining tasks without having access to the original data. However, the methods proposed so far only target a small to medium number of variables, since their estimates rely on representing all the interdependencies between the variables of the data. High-dimensional data streams, which are becoming more and more frequent due to increasing numbers of interconnected devices, are, therefore, pushing these methods to their limits. To mitigate these limitations, we present an approach that projects the original data stream into a vector space and uses a set of representatives to provide an estimate. Due to the structure of the est…

Data Mining Algorithms for Knowledge Extraction

2020

In this paper, we study the methods, techniques, and algorithms used in data mining, and from the studied algorithms, we emphasized the clustering algorithms, more precisely on the K-means algorithm. This algorithm was first studied using the Euclidean distance, then modifying the distance between the clusters using the distances Mahalanobis and Canberra. After implementing the algorithms in C/C++, we compared the clustering of the three algorithms, after which we modified them and studied the distance between the clusters.

PRINCIPAL POLYNOMIAL ANALYSIS

2014

© 2014 World Scientific Publishing Company. This paper presents a new framework for manifold learning based on a sequence of principal polynomials that capture the possibly nonlinear nature of the data. The proposed Principal Polynomial Analysis (PPA) generalizes PCA by modeling the directions of maximal variance by means of curves instead of straight lines. Contrarily to previous approaches PPA reduces to performing simple univariate regressions which makes it computationally feasible and robust. Moreover PPA shows a number of interesting analytical properties. First PPA is a volume preserving map which in turn guarantees the existence of the inverse. Second such an inverse can be obtained…

Multimedia Retrieval by Means of Merge of Results from Textual and Content Based Retrieval Subsystems

2010

The main goal of this paper it is to present our experiments in ImageCLEF 2009 Campaign (photo retrieval task). In 2008 we proved empirically that the Text-based Image Retrieval (TBIR) methods defeats the Content-based Image Retrieval CBIR "quality" of results, so this time we developed several experiments in which the CBIR helps the TBIR. The TBIR System [6] main improvement is the named-entity sub-module. In case of the CBIR system [3] the number of low-level features has been increased from the 68 component used at ImageCLEF 2008 up to 114 components, and only the Mahalanobis distance has been used. We propose an ad-hoc management of the topics delivered, and the generation of XML struct…

Hyperspectral detection of citrus damage with Mahalanobis kernel classifier

2007

Presented is a full computer vision system for the identification of post-harvest damage in citrus packing houses. The method is based on the combined use of hyperspectral images and the Mahalanobis kernel classifier. More accurate and reliable results compared to other methods are obtained in several scenarios and acquired images.

Clustering-Based Protocol Classification via Dimensionality Reduction

2015

We propose a unique framework that is based upon diffusion processes and other methodologies for finding meaningful geometric descriptions in high-dimensional datasets. We will show that the eigenfunctions of the generated underlying Markov matrices can be used to construct diffusion processes that generate efficient representations of complex geometric structures for high-dimensional data analysis. This is done by non-linear transformations that identify geometric patterns in these huge datasets that find the connections among them while projecting them onto low dimensional spaces. Our methods automatically classify and recognize network protocols. The main core of the proposed methodology…

Accelerated Proximal Gradient Descent in Metric Learning for Kernel Regression

2018

The purpose of this paper is to learn a specific distance function for the Nadayara Watson estimator to be applied as a non-linear classifier. The idea of transforming the predictor variables and learning a kernel function based on Mahalanobis pseudo distance througth an low rank structure in the distance function will help us to lead the development of this problem. In context of metric learning for kernel regression, we introduce an Accelerated Proximal Gradient to solve the non-convex optimization problem with better convergence rate than gradient descent. An extensive experiment and the corresponding discussion tries to show that our strategie its a competitive solution in relation to p…

Use of QSAR methods for predicting the chemiluminescent behaviour of organic compounds upon reaction with potassium permanganate in an acid medium

2009

In previous work, molecular connectivity computations were successfully used to predict the chemiluminescent behaviour of organic compounds upon reaction with common strong oxidants and the native fluorescence too; both of them in a liquid phase. The obtained results were used to develop new analytical procedures to the given compounds. For the first time, connectivity methods were used for a purely analytical purpose. In this work, we went deeper into the knowledge of direct chemiluminescence processes by using molecular connectivity in the form of QSAR methods to predict the chemiluminescence intensity produced by reactions between organic compounds (pharmaceuticals mainly) and potassium …