Robust Semisupervised Nonnegative Local Coordinate Factorization for Data Representation

<table class="figure-group"><tr class="fig-image" id="a"><td><object data="https://static.hindawi.com/articles/complexity/volume-2018/7963210/figures/7963210.fig.006a.svgz" name="7963210.fig.006a" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(a) </b>Acc results</td></tr><tr class="fig-image" id="b"><td><object data="https://static.hindawi.com/articles/complexity/volume-2018/7963210/figures/7963210.fig.006b.svgz" name="7963210.fig.006b" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(b) </b>NMI results</td></tr></table>

<div>Clustering Acc and NMI curves of the compared methods under different numbers of labeled samples for the leukemia dataset.</div>

Complexity

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Figure 6

Figure 6: Robust Semisupervised Nonnegative Local Coordinate Factorization for Data Representation 