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Generally, J is far less than the dimensionality of the inputting sparse representation coefficient vector.
And then they use the representation coefficient matrix to construct the affinity graph for subspace segmentation.
Figure 1 gives a pictorial illustration for the JSR coefficient matrix with different parameter K. Dimensionality of each sparse representation coefficient vector is 10, and every entry in sparse representation coefficient vector is represented with one block.
This model can be solved by computing the sparse representation coefficient vectors one by one as well.
After we get the final result of representation coefficient matrix Z, we can use is as a kind of similarity to construct an affinity graph (|Z| + |Z T |).
The low-rank matrix recovery strategy is introduced to explore the final support samples and its corresponding sparse representation coefficient matrix in the second stage.
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Secondly, sparse level of representation coefficients is promoted largely.
Nonnegative linear reconstruction measure can produce the sparse nonnegative representation coefficients which are concentrated on the similar training samples.
This objective function can be effectively solved by an iteration algorithm, and then the representation coefficients and motion parameters are obtained at the same time.
Then, the sparse representation coefficients matrix of training sample sets for specific cloud type was used to form a projection subspace.
Based on this idea, we design a novel method, differentiated representation method, which uses a mathematically tractable means to make representation coefficients on a test sample generated from a class quite unsuitable for other classes.
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