Read this paper for more details: http://psiexp.ss.uci.edu/research/papers/SteyversShiffrinNelsonFormatted.pdf WAS_BIGS2SOLUTION.mat ===================== Contains SVD solution to the full 5018 x 5018 word association dataset. Take cosine similarity between vectors. The SVD is based on S(2)_ij = S(1)_ij + sum_k[ S(1)_ik * S(1)_kj ] where S(1)_ij = A_ij + A_ji and A_ij is the associative strength between item i and j. N 1x1 8 double --> number of words V 5018x400 16057600 double --> V( i , : ) contains the vector for word i (not normalized). W 1x5018 623076 cell --> W{ i } contains word i WAS_S2.mat ========== Contains SVD solution to a subset of 2500 x 2500 word association dataset. Take cosine similarity between vectors. The SVD is based on S(2)_ij = S(1)_ij + sum_k[ S(1)_ik * S(1)_kj ] where S(1)_ij = A_ij + A_ji and A_ij is the associative strength between item i and j. N 1x1 8 double --> number of words V 2500x500 10000000 double --> V( i , : ) contains the vector for word i (not normalized). W 1x2500 307642 cell --> W{ i } contains word i WAS_MDS.mat =========== Contains MDS solution to a subset of 2500 x 2500 word association dataset. Take Euclidean distance between vectors. The MDS is based on distances T_ij = -log[ S(1)_ik * S(1)_kl * ... * S(1)_qj ] where S(1)_ij = A_ij + A_ji and A_ij is the associative strength between item i and j. N 1x1 8 double --> number of words V 2500x500 10000000 double --> V( i , : ) contains the vector for word i (not normalized). W 1x2500 307642 cell --> W{ i } contains word i