Energy-efficient sample selection method based on complexity

An energy-efficient sample selection method based on complexity, wherein the lightweight data set is constructed by efficient sample selection for model training, and the method comprises two stages of inter-class sampling and intra-class sampling, wherein:

• inter-class sampling, inter-class sampling is performed through reverse self-paced learning with diversity, specifically the reverse self-paced learning with diversity algorithm is shown below:
• input:
• target data set D
• output:
• model parameter w,
• 1) i←0;
• ←+∞;
• ←+∞;
• 2) if there is no prior clustering, then
• 3) set the number of clusters to the number of classes;
• 4) end if;
• 5) while not converged do;
• 6) update w*=argminwE(w, v*;
• λ, γ);
• 7) use Algorithm 1 to update v□=argminvE(w□, v;
• λ, γ);
• 8) λ←(1−e−i)λ;
• γ←(1−e−i)γ;
• 9) i++;
• 10) end while;
• 11) return w=w□;
• intra-class sampling is performed by a density-based sampling strategy, specifically an efficient intra-class sampling algorithm based on complexity:
• input:
• target data set{circumflex over (D)}={(Xi, ci, loss)}, sampling rate ζ;
• output:
• synthetic data set Ψ;
• 1) Ψ←{ };
• 2) for i=1 to ∥C∥do;
• 3) select all samples belonging to ci, denoted as (ci);
• 4) [cNum, cCenters, cSamples]=meanshift({circumflex over (D)}(ci));
• 5) for j=1 to cNum do;
• 6) if ∥cSamples(j)∥>threshold, then:
• 7) Ψ←Ψ□metropolis-hastings(cCenters(j), cSamples(j), ζ);
• 8) else;
• 9) Ψ←Ψ□cSamples(j);
• 10) end if;
• 11) end for;
• 12) end for;
• 13) return Ψ.