Euclidean Distance
Face Recognition using Euclidean Distance Correlation Algorithm
... In this section of paper, KPCA (Kernel PCA), KFD which is (KPCA plus LDA), CKFD (Compete KFD) and GKFD (Generalized KFD) are analyzed, compared with our proposed Euclidean Distance Correlation Based (EDCB) ...
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Hardware Implementation of Speech Recognition Using MFCC and Euclidean Distance
... sample. Euclidean distance measures the minimum distance and its result of the test program that shows the correct spoken word in the command window of code composer ...
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Distance Based Similarity Between Text Documents Using Euclidean Distance
... the distance between the different ...calculate distance between different ...the distance gives output (that is the recommendation of the items) in least time and ...optimum distance value. ...
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EUCLIDEAN DISTANCE METHOD: A TOOL FOR SPEECH RECOGNITION
... Abstract: The aim of the project is to develop a prototype system for ‘speech recognition’ technique. The algorithm creates its own dictionary of basic phonemes. Input signal is broken into segments and compared with the ...
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Structure method for solving the nearest Euclidean distance matrix problem
... a Euclidean distance matrix is used to represent the squares of distances between the atoms of a molecular ...a distance matrix F which, because of data errors, may not be ...
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Significant Sentence Extraction by Euclidean Distance Based on Singular Value Decomposition
... In this subsection, we will illustrate how to extract the significant sentences by examples. In [7], the importance of each sentence is computed by repeatedly summing 1 for each occurrence of significant terms in the ...
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Discrimination of Epileptic and Normal EEG Using Euclidean Distance Method
... The test patient taken now is an epileptic patient, and its minimum Euclidean distance with the entire database is calculated. We get the minimum Euclidean distance of 2.256E+12 for the ...
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Penalised Euclidean distance regression
... penalty is the main contribution of the paper. The advantage of our approach is that we are able to provide a pivotal recovery property, and in addition gain the grouping property of the Elastic Net (where regression ...
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A Modification in Euclidean Distance Calculator Circuit with the Aim of English-Numeral Recognition
... Abstract — In this brief, a current-mode Euclidean distance calculator circuit which acts as a numeral recognizer is proposed. This recognizer circuit consists of three sub- circuits: a subtracter-rectifier ...
5
General minimum Euclidean distance-based precoder for MIMO wireless systems
... In this article, we investigate the linear precoder based on the maximization of the minimum Euclidean distance between two received data vectors. This new precoding matrix is expressed as the product of a ...
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Handwritten Character Recognition Using Multiresolution Technique and Euclidean Distance Metric
... In the present paper, the problem of handwritten character recognition has been tackled with multiresolution technique using Discrete wavelet transform (DWT) and Euclidean distance metric (EDM). The ...
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An analysis of Euclidean Distance preserving perturbation for Privacy Preserving Data Mining
... Recently, distance preserving data perturbation has gained attention because it mitigates the privacy/accuracy trade-off by guaranteeing perfect ...e.g., distance-based clustering and k-nearest neighbor ...
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Euclidean Distance Distortion Based Robust and Blind Mesh Watermarking
... Generally, the mesh watermarking techniques inserts the watermark by modifying the geometry of the polygonal models [11], [12]. Some of these algorithms are spatial approaches and the embedding process modifies the ...
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Automatic object recognition based on Euclidean distance restricted auto encoder
... Sheng et al, [1] proposed a new Euclidian distance restricted neural network for target recognition from SAR data. Various other researchers’ including Tejada et al, [5], Gani et al, [6] and Kang et al, [7] has ...
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Properties of Euclidean and non-Euclidean distance matrices
... The terms metric and nonmetric can be misleading in the context of multidimensional scaling, for in both cases a metric (usually Euclidean) is fitted to the dat[r] ...
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2D Face Recognition Based on PCA & Comparison of Manhattan Distance, Euclidean Distance & Chebychev Distance
... that describes the contribution of each eigenface in representing the input face image, treating the eigenfaces as a basis set for face images. The vector may then be used in a standard pattern recognition algorithm to ...
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Enhanced self organizing map with particle swarm optimization for classification problems
... This study investigates the capabilities of enhanced SOM with Particle Swarm Optimization (ESOMPSO) to solve the classification problems. The performance of standard SOM, ESOM, SOMPSO and ESOMPSO using various ...
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Effect of Dynamic Time Warping using different Distance Measures on Time Series Classification
... different distance measures such as the Euclidean distance, Manhattan distance, Normalized Euclidean distance and Canberra Distance, to test its effect on the ...
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Effectively and Efficiency Consideration for Spatial Database
... using Euclidean Distance to compute the similarity between them, and retrieve the sets of object from database based on Euclidean distance that make this situation is happen and ...
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Acculturation trajectories and quality of life in South African immigrants living in New Zealand : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Clinical Psychology at Massey University, Palmerston North, New Zealand
... squared Euclidean distance measures were used to capture how two cases differed in their profile on the variables (see appendices H-O for Binary squared Euclidean distance matrices for each ...
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