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In the literature, a number of methods have been proposed for semi-supervised learning. Recently, graph-based methods of semi-supervised learning have become popular because of their capability of handling large amounts of unlabeled data. However, the existing graph based semi-supervised learning algorithms do not optimize the process of selecting better labeled data. We have developed a new selective...
The sparse representation of signals with respect to an over-complete dictionary has been of recent interest in a broad range of applications. One of the most used methods for obtaining sparse codes, the Lasso problem, becomes computationally costly for large dictionaries and this hinders the use of this approach to large-scale decision tasks. Recently, dictionary screening has been used to address...
SIFT-based methods have been widely used for scene matching of photos taken at particular locations or places of interest. These methods are typically very time consuming due to the large number and high dimensionality of features used, making them unfeasible for use in consumer image collections containing a large number of images where computational power is limited and a fast response is desired...
Sub-health state is a low-quality status between health and disease. The aim of this study was to determine which factors and/or combination of factors could be predictive of sub-health state. In this paper, we carried out a clinical epidemiology survey and obtained two datasets both of which include 50 symptoms in report. The Dataset 1 consists of 572 samples, of which 523 cases were in sub-health...
The widespread utilization of digital visual media has motivated many research efforts towards efficient search and retrieval from large photo collections. Traditionally, SIFT feature-based methods have been widely used for matching photos taken at particular locations or places of interest. These methods are very time-consuming due to the complexity of the features and the large number of images...
View-invariant representation has been shown to be a powerful tool in classification and retrieval of motion events due to camera motions. Traditional null space representation is invariant only for linear transformations and does not yield high accuracy for camera with non-linear motions. In this paper, we propose a novel general framework for non-linear kernel space invariant representation (NKSI),...
In this paper, we propose a novel general framework for tensor based null space affine invariants, namely, tensor null space invariants (TNSI) with a linear classifier for high order data classification and retrieval. We first derive TNSI, which is perfectly invariant to multidimensional affine transformations due to camera motions for multiple motion trajectories in consecutive motion events. We...
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