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Table 4 Time complexities of different SVM-based methods for remote homology detection

From: A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis

Methods Time complexities
SVM-Top-n-gram-combine O (n2l) + O (nml) + O (n2m)
SVM-Bprofile O (n2l) + O (nml) + O (n2m)
SVM-Ngram O (nl) + O (nml) + O (n2m)
SVM-Pattern O (nl lognl+n2l2m) + O (nml) + O (n2m)
SVM-Motif O (n2l2) + O (nml) + O (n2m)
SVM-Top-n-gram-combine-LSA O (n2l) + O (nml) + O (nmt) + O (n2R)
SVM-Bprofile-LSA O (n2l) + O (nml) + O (nmt) + O (n2R)
SVM-Ngram-LSA O (nl) + O (nml) + O (nmt) + O (n2R)
SVM-Pattern-LSA O (nl lognl+n2l2m) + O (nml) + O (nmt) + O (n2R)
SVM-Motif-LSA O (n2l2) + O (nml) + O (nmt) + O (n2R)
SVM-Pairwise O (n2l2) + O (n3)
SVM-LA O (n2l2) + O (n3)
Profile O (n2l) + O (n2l)
SW-PSSM O (n2l) + O (n2l2)
  1. Where n is the number of training samples, l is the length of the longest training sequence, m is the total number of the words of each building block, t is the minimum of n and m and R is the length of the latent semantic feature vector.