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Figure \[fig:bima3\] exhibits a graphical comparison of the runtimes of the four algorithms. We observe that Bi-Asymmetric MA$_{12}$ and Bi-Asymmetric MA$_{17}$ algorithms outperform the original MA algorithm by a factor of ten.

![Runtime comparison: Bi-Asymmetric MA$_{12}$ and Bi-Asymmetric MA$_{17}$ algorithms are better than MA algorithm by a factor of ten.[]{data-label=”fig:bima3″}](tidr1217_time “fig:”){width=”50.00000%”}![Runtime comparison: Bi-Asymmetric MA$_{12}$ and Bi-Asymmetric MA$_{17}$ algorithms are better than MA algorithm by a factor of ten.[]{data-label=”fig:bima3″}](tidr1217_time_new “fig:”){width=”50.00000%”}

A comparison with the state-of-the-art
————————————–

We use the COIL100 database, a well-known image sequence database that includes $100$ images of $100$ objects (samples) from $7$ classes. Since our MA algorithms are designed for class-specific recommendation task, we are only interested in comparing with the state-of-the-art algorithms where they apply their models to the target classes. For example, the first one compares the MA with the state-of-the-art algorithms such as BCTD [@Campos2011; @Oliveira2015], CSIM [@Oliveira2015], iCARM [@Ma2015]. The second one compares the MA with the state-of-the-art algorithms such as cVEP [@Smeulders2011] and Block Coordinate Feature Learning (BCFL) [@Liu2014].

Table \[table:COIL100\] shows the results. The first three algorithms compare the MA with respect to the state-of-the-art algorithms for models such as the Mahalanobis distance (MD) [@Campos2011; @Oliveira2015], the Euclidean distance (ED) [@Campos2011; @Oliveira2015; @Ma2015