Nonnegative Matrix Factorizations Performing Object Detection and Localization

<table>Illustration of the learnt bases (with <svg height="10.9125" id="M201" style="vertical-align:-0.17555pt" version="1.1" viewbox="0 0 41.512501 10.9125" width="41.512501" xmlns="http://www.w3.org/2000/svg">
<g transform="matrix(1.25,0,0,-1.25,0,10.9125)">
<g transform="translate(72,-63.27)">
<text transform="matrix(1,0,0,-1,-71.95,63.5)">
<tspan style="font-size: 12.50px; " x="0" y="0">𝑟</tspan>
<tspan style="font-size: 12.50px; " x="8.5645552" y="0">=</tspan>
<tspan style="font-size: 12.50px; " x="20.604944" y="0">8</tspan>
<tspan style="font-size: 12.50px; " x="26.856443" y="0">0</tspan>
</text>
</g>
</g>
</svg>) of the USPS dataset obtained via (a) NMF, (b) LNMF, (c) NMFsc,and  (d) DLPP.</table>

Applied Computational Intelligence and Soft Computing

fig6

Figure 6

Figure 6: Nonnegative Matrix Factorizations Performing Object Detection and Localization 