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Run .Related: Create Barcode SSRS , Generate Barcode Crystal how to, ASP.NET Barcode Generating By recognizing the fact that tensor objects are naturally multidimensional objects instead of one-dimensional objects, multilinear feature extraction algorithms [2, 14, 18 20] operating directly on the tensorial representations rather than their vectorized versions are emerging, partly due to the recent development in multilinear algebra [1, 21, 22] The multilinear principal component analysis (MPCA) framework [14]2 attempts to determine a multilinear projection that projects the original tensor objects into a lower-dimensional tensor subspace while preserving the variation in the original data It can be further extended through the combination with classical approaches [14, 18, 25] and has achieved good results when applied to the gait recognition problem Nonetheless, MPCA is an unsupervised method and the class information is not used in the feature extraction process There has been a growing interest in the development of supervised multilinear feature extraction algorithms. 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High-performance .Related: A two-dimensional linear discriminant analysis (2DLDA) was proposed in reference 26; and later a more general extension, the Discriminant Analysis with Tensor Representation (DATER),3 was proposed in reference 2 They maximize a tensor-based scatter ratio criterion and the application to the face recognition problem showed better recognition results than linear discriminant analysis (LDA) In reference 19, a so-called general tensor discriminant analysis (GTDA) algorithm is proposed by maximizing a scatter difference criterion, and it is used as a preprocessing step in tensorial gait data classi cation [19] All these methodologies are based on the tensor-to-tensor projection (TTP) The so-called Tensor Rank-one Discriminant Analysis (TR1DA) algorithm [27, 28], which uses the scatter difference criterion, obtains a number of rank-one projections from the repeatedly calculated residues of the original tensor data and it can be viewed as a tensor-to-vector projection (TVP). 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Recognition results, using the CMU database, for some single-view and multipleview methods are presented in Table 21.2. We chose to use the simplest possible template that is, the average silhouette for the assessment of these methods since, despite its simplicity, it is very ef cient. Since the average silhouettes of the side (east) view and the frontal (south) view contain more discriminative information than other views, the results using these two views are the best among the ones using single views. It can be seen that, if we combine multiple views by averaging the relevant distances, the result is worse than in the case where only the east or the south view. Qr Bidimensional Barcode barcode library for .net Using Barcode decoder for .NET Control to .Related: in applications that can display a dynamic URL such . 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