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To tackle the noise influence of the dataset, only k largest entries of each representation are kept in the subspace clustering.
Utilizing this knowledge, the estimated set of assigned signatures (widehat {mathcal {T}}_{mathrm {A}}) is dictated by the K largest entries of signal recovery (widehat {mathbf {x}}).
For two vectors (mathbf{x},mathbf{y}in[alpha,beta]^{l}), let (x_{[i]},y_{[i]}) denote the ith largest entries of x and y, respectively.
And then, Υ l is identified as the set of coordinates corresponding to the k largest entries of h l and Λ is expanded as Λ=Λ∪Υ l.
Proof 2. Consider ϕi+1 is the best possible sparse approximation with K-sparse (because it is formed by retaining the K largest entries in ϕ t i and setting others to zero) and ϕ i is also K-sparse.
The light grey curves show the legacy approach used in [12 14] that assumes to know K; there, the indices of the K largest entries in the AMP recovery compose (widehat {mathcal {T}}_{mathrm {A}}).
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