Nonnegative Matrix Factorizations Performing Object Detection and Localization

<table>Behaviour of the orthogonality error for matrix <svg height="10.575" id="M215" style="vertical-align:-0.20064pt" version="1.1" viewbox="0 0 17.9625 10.575" width="17.9625" xmlns="http://www.w3.org/2000/svg">
<g transform="matrix(1.25,0,0,-1.25,0,10.575)">
<g transform="translate(72,-63.54)">
<text transform="matrix(1,0,0,-1,-71.95,63.79)">
<tspan style="font-size: 12.50px; " x="0" y="0">𝑊</tspan>
</text>
</g>
</g>
</svg> during the learning iterations on the CarData dataset: (a) rank value <svg height="10.9125" id="M216" 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">2</tspan>
<tspan style="font-size: 12.50px; " x="26.856443" y="0">0</tspan>
</text>
</g>
</g>
</svg>, (b) rank value <svg height="11.1" id="M217" style="vertical-align:-0.17555pt" version="1.1" viewbox="0 0 49.337502 11.1" width="49.337502" xmlns="http://www.w3.org/2000/svg">
<g transform="matrix(1.25,0,0,-1.25,0,11.1)">
<g transform="translate(72,-63.12)">
<text transform="matrix(1,0,0,-1,-71.95,63.35)">
<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">1</tspan>
<tspan style="font-size: 12.50px; " x="26.856443" y="0">1</tspan>
<tspan style="font-size: 12.50px; " x="33.107944" y="0">5</tspan>
</text>
</g>
</g>
</svg>.</table>

Applied Computational Intelligence and Soft Computing

fig10

Figure 10

Figure 10: Nonnegative Matrix Factorizations Performing Object Detection and Localization 