In matrix learning, vector patterns are simply transformed into matrix ones by some reshaping techniques such as from 100 × 1 to 20 × 5. Unfortunately, the techniques are random and fail in some cases. To this end, a matrix learning machine with interpolation mapping named IMAT for short is proposed. IMAT interpolates each feature of the original vector pattern into its corresponding k-means slots so as to generate a matrix pattern with more structural information. Furthermore, the pairwise information of every two features can be introduced into the IMAT. After that, the IMAT can be applied into matrix-based classifiers. The contributions of the proposed IMAT are listed as follows. (i) the IMAT can extract more intrinsic structural information compared with those random techniques reshaping the vector into a matrix. (ii) The IMAT is supposed to be reasonably and naturally embedded into matrix-based classifiers. In the experiments, the authors' previous work is adopted, a matrix-based classifier named MatMHKS, to examine the IMAT on some UCI datasets. The results verify the superior classification performance of IMAT.
Because a joint transform correlator can be used as a general optical signal processor, complex-impulse-response implementations in the spatial domain are often requested. We introduce a position-encoding technique with which complex-valued references for the joint transform correlator can be obtained with an amplitude-modulated spatial light modulator. A proof-of-concept experiment is also provided.
We present the design of a bipolar composite filter (BCF) by a simulated annealing algorithm. By minimizing the energy function of the system, we construct an out-of-plane rotation-invariant bipolar filter. We show that the BCF offers high pattern discrimination capability and can easily be implemented with an electronically addressed spatial light modulator.
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