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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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sort( start, end, compare );

.

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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start and end are pointers to elements of an array of strings The sort() function sorts the array elements between start and end compare defines the comparison operation to be used when comparing two strings within the array What implementation should be used for compare We may want to sort the strings within the array in lexicographic order that is, in the same manner as words are sorted within a dictionary; or we may want to sort them by length so that the shorter strings are placed at the front of the array and the longer ones at the end Some facility for specifying alternative comparison operations is needed (Note that 12 describes the sort() function and other general algorithms provided by the C++ standard library In this section, to illustrate the use of function pointers, we write our own sort() function, which is a simplified version of the function in the C++ standard library) One strategy to resolve these requirements is to have the third argument compare be a function pointer specifying the comparison function to be used To simplify the use of the sort() function without restricting its flexibility, we may want to specify a default comparison operation to be used in the majority of cases Let's assume that the most common case is to sort the strings in lexicographic order and that the default argument would specify a comparison operation that would use the compare() function for strings (this function is first introduced in Section 610) This section considers how to implement our sort() function using function pointers The Type of a Pointer to Function How can a pointer to function be declared What will the parameter that takes a pointer to function as an argument look like Here is the definition of the lexicoCompare() function used to compare two strings lexicographically:.

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#include <string> int lexicoCompare( const string &s1, const string &s2 ) { return s1compare(s2); }

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If all characters in the strings s1 and s2 are equal, then lexicoCompare() returns 0; otherwise, if the string represented by the first parameter is less than the string represented by the second parameter, a negative number is returned; if it is greater, a positive number is returned A function's name is not part of its type A function's type is determined only by its return type and its parameter list A pointer to lexicoCompare() must point to a function with the same type (with the same return type and the same parameter list) as lexicoCompare() Let's try:

This greedy approach is a heuristic method originally proposed in reference 29 for tensor approximation In reference 30, an uncorrelated multilinear discriminant analysis (UMLDA) approach is proposed to extract uncorrelated features through TVP The extensions of linear graph-embedding algorithms were also introduced similarly in references 31 35..

// ok: still wrong, but now it compiles! // explicit cast calls attention to the assignment // when program fails, casts should be among first // constructs to examine pc = static_cast< char* >( pv ); // still a disaster char *pstr = new char[ strlen( pc )+1 ]; strcpy( pstr, pc ); }

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