Daily a vast amount of capital is being traded through the stock market. Apart from using holographic neural network for prediction, if we think from a different perspective, we have to fully harness the input data, so as to get good prediction result. Here the stocks historic data is preprocessed and bases of stocks for prediction are formed using minimum spanning tree and correlation. Fitness of each group of bases in checked by calculating the Mahalanobis distance.
Topics: Holographic, Prim’s, Correlation, Mahalanobis Distance
The purpose of this thesis was to review cost estimating relationships that have been developed and used for aircraft airframe costs, to identify existing problems, and where appropriate, to suggest alternatives for the future application of cost estimating relationship to aircraft airframes. Mahalanobis distance was explored as a leans of complementing the more traditional statistical measures for regression analysis. This study supports the conclusion that cost estimating relationships should...
Topics: Cost estimating relationships, Aircraft airframe costs, Mahalanobis distance
Face detection has advanced dramatically over the past three decades. Algorithms can now quite reliably detect faces in clutter in or near real time. However, much still needs to be done to provide an accurate and detailed description of external and internal features. This paper presents an approach to achieve this goal.Previous learning algorithms have had limited success on this task because the shape and texture of facial features varies widely under changing expression, pose and...
Topics: Line Edge Mapping, Phong Shading, Fast Fourier Transform, Mahalanobis Distance Vector
We initiate a new distance metric learning technique recognized as ambiguously supervised structural metric learning to find out discriminative Mahalanobis distance metric that is based on weak supervision data. For improving the performance, two affinity matrices are combined to get a fused affinity matrix which is used for face naming. When specified a collection of images, in which each of the image contains numerous faces and is linked by few names in corresponding caption, the purpose of...
Topics: Images, Regularized low-rank, Affinity matrices, Face naming, Mahalanobis distance metric, Computer...
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In recent times, researchers in the remote sensing community have been greatly interested in utilizing hyperspectral data for in-depth analysis of Earth’s surface. In general, hyperspectral imaging comes with high dimensional data, which necessitates a pressing need for efficient approaches that can effectively process on these high dimensional data. In this paper, we present an efficient approach for the analysis of hyperspectral data by incorporating the concepts of Non-linear manifold...
Topics: Remote Sensing, Hyperspectral, Non- linear Dimensionality Reduction (NLDR), Mahalanobis distance,...
From the bitsavers.org collection, a scanned-in computer-related document. mit :: ai :: aim :: AIM-1521
Topics: face, distance, pattern, patterns, vector, image, canonical, detection, gaussian, metric, canonical...
