which correspond to the row vector of Fig.7a, correlate with each other. We define the cosine similarity matrixC, where the element of thei-th row andj-th column is the cosine similarity between the concept vectors of labelsiandj:
Next, the indexer method creates word lists and word count vectors. [word_lists,word_counts] = LSA.indexer(tokenized); Then we create a document-term frequency matrix from these using docterm. The minimum frequency is set to 2 and that drops any words that only occur once through the en...