Concise comparative summaries (CCS) of large text corpora with a human experiment\thanksrefT1
Table 3: Computational speed chart. Average running times for the four feature selection methods over all subjects considered. Second column includes time to generate y and adjust X. Final column is percentage increase in total time over Co-occurrence, the baseline method
['[EMPTY]', '[BOLD] Phrase selection (sec)', '[BOLD] Total time (sec)', '[BOLD] Percent increase']
The primary advantages of Co-occurrence and Correlation Screening are that they are fast, scalable and easily distributed across multiple cores for parallel processing. Unfortunately, as they score each feature independently from the others, they cannot take advantage of any dependence between features to aid summarization. The Lasso and L1LR can, to a certain extent. The down side is that the sparse methods are more computationally intensive than Co-occurence and Correlation Screening. However, this could be mitigated by, for example, moving to a parallel computing environment or doing clever preprocessing such as safe feature elimination [El Ghaoui, Viallon and Rabbani