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For the traditional fingerprinting-based positioning approach, it is essential to collect measurements at known locations as reference fingerprints during a training phase, which can be time-consuming and labor-intensive. This paper proposes a novel approach to track a user in an indoor environment by integrating similarity-based sequence and dead reckoning. In particular, we represent the fingerprinting...
Learning-based anomaly detection method is often subject to inaccuracies due to noise, small sample size, bad choice of parameter for the estimator, etc. We propose a novel method using higher-order feature, based on the sequence nonparametric test to assess the reliability of the estimation. The method allows an expert to discover informative features for separation of normal and attack instances...
In this paper, we apply a new linear correlation attribute reduction algorithm to feature selection. The algorithm is valuable when the features are marginally unrelated but jointly related to the response variable. A new technique is introduced to remove redundant attributes and it is effective to reduce the false selection rate in the feature selection stage. We train and test the new algorithm...
This paper proposes a two-phase algorithm of image projection discriminant analysis. The new discriminant method is composed of feature extraction by on maximum margin criterion (MMC) and Fisher discriminant analysis (FDA). The algorithm includes two stages: firstly, the feature extraction based on maximum margin criterion (MMC) is employed to condense the dimension of image matrix; Then Fisher discriminant...
Transforms on the spectral domain are often used to compress and extract information of sample features for the supervised classification on hyperspectral images. On condition that the ground samples are all in mass each, there are more obvious advantages on sample classification with obscure spectral features when using Maximum Noise Fraction (MNF) transform, or named two cascaded principal components...
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