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Toward a Fundamental Theory of Optimal Feature Selection: Part II-Implementation and Computational Complexit
January 1987 (vol. 9 no. 1)
pp. 29-38
Salvatore D. Morgera, Department of Electrical Engineering, McGill University, 3480 University Street, Montreal, P.Q. H3A 2A7, Canada.
Certain algorithms and their computational complexity are examined for use in a VLSI implementation of the real-time pattern classifier described in Part I of this work. The most computationally intensive processing is found in the classifier training mode wherein subsets of the largest and smallest eigenvalues and associated eigenvectors of the input data covariance pair must be computed. It is shown that if the matrix of interest is centrosymmetric and the method for eigensystem decomposition is operator-based, the problem architecture assumes a parallel form. Such a matrix structure is found in a wide variety of pattern recognition and speech and signal processing applications. Each of the parallel channels requires only two specialized matrix-arithmetic modules. These modules may be implemented as linear arrays of processing elements having at most O(N) elements where N is the input data vector dimension. The computations may be done in O(N) time steps. This compares favorably to O(N3) operations for a conventional, or general, rotation-based eigensystem solver and even the O(2N2) operations using an approach incorporating the fast Levinson algorithm for a matrix of Toeplitz structure since the underlying matrix in this work does not possess a Toeplitz structure. Some examples are provided on the convergence of a conventional iterative approach and a novel two-stage iterative method for eigensystem decomposition.
Citation:
Salvatore D. Morgera, "Toward a Fundamental Theory of Optimal Feature Selection: Part II-Implementation and Computational Complexit," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 9, no. 1, pp. 29-38, Jan. 1987, doi:10.1109/TPAMI.1987.4767870
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