Parallel Framework for Dimensionality Reduction of Large-Scale Datasets

<div class="floats-full-page floats-full-page-other"><h2>Table 4</h2><div class="floats-full-page-caption">Component-wise run time in seconds for varying problem sizes, and <svg height="12.2707pt" id="M316" style="vertical-align:-3.57194pt" version="1.1" viewbox="-0.0498162 -8.69876 49.8265 12.2707" width="49.8265pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><path d="M570 304C570 398 525 448 414 448C385 448 343 445 312 434L329 511L321 518C297 504 262 482 244 460L233 411C195 397 159 381 128 358L135 332C160 347 189 360 224 373L111 -147C97 -210 84 -218 17 -231L13 -257L254 -247L259 -218L233 -216C183 -212 177 -202 189 -142L218 -1C238 -10 266 -12 283 -12C351 3 429 48 483 105C543 168 570 242 570 304ZM482 289C482 161 380 33 304 33C278 33 248 51 233 69L303 396C326 400 352 403 369 403C428 403 482 380 482 289Z" id="g113-113"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="593" vert-adv-y="593"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,11.82,0)"><path d="M535 323V373H52V323H535ZM535 138V188H52V138H535Z" id="g117-34"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="587" vert-adv-y="587"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,23.558,0)"><path d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z" id="g113-50"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="480" vert-adv-y="480"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,30.061,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z" id="g113-49"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="480" vert-adv-y="480"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,36.564,0)"><path d="M412 140C382 77 369 73 315 73H129L270 222C362 320 402 379 402 466C402 571 322 635 234 635C177 635 130 609 99 576L42 495L64 475C90 514 133 568 201 568C274 568 318 519 318 435C318 349 255 267 193 193C144 135 87 78 32 23V0H405C417 45 427 89 440 131L412 140Z" id="g113-51"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="480" vert-adv-y="480"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,43.068,0)"><path d="M456 178V225H360V632H320C217 496 115 347 20 206V178H280V106C280 40 276 34 189 27V0H445V27C364 34 360 39 360 106V178H456ZM280 225H82C149 335 214 431 278 520H280V225Z" id="g113-53"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="480" vert-adv-y="480"></glyph.data></g></svg> and <svg height="9.21094pt" id="M317" style="vertical-align:-0.2129908pt" version="1.1" viewbox="-0.0498162 -8.99795 58.7066 9.21094" width="58.7066pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><path d="M743 375C743 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635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z" id="g113-49"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="480" vert-adv-y="480"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,38.907,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z" id="g113-49"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="480" vert-adv-y="480"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,45.41,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z" id="g113-49"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="480" vert-adv-y="480"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,51.914,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z" id="g113-49"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="480" vert-adv-y="480"></glyph.data></g></svg>.</div><div class="floats-full-page-content"><table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="6"><hr /></td></tr><tr class="thead"><td align="left"><span style="width: 6.92631ptpx;"><svg height="6.42315pt" id="M318" style="vertical-align:-0.2129903pt" version="1.1" viewbox="-0.0498162 -6.21016 6.92631 6.42315" width="6.92631pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><path d="M495 86L479 114C446 82 419 66 409 66C401 66 401 72 406 97C420 166 436 231 453 297C489 435 454 448 428 448C406 448 384 439 354 422C305 394 222 327 161 247H159L183 345C200 415 194 448 173 448C143 448 82 410 23 351L38 325C64 349 95 371 105 371C111 371 116 365 109 336L25 -4L31 -12C50 -4 77 3 107 9C119 69 132 122 145 168C197 254 321 381 370 381C387 381 393 374 378 305L329 95C309 17 320 -12 345 -12C372 -12 430 19 495 86Z" id="g113-111"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="502" vert-adv-y="502"></glyph.data></g></svg></span></td><td align="center"> 2048 </td><td align="center"> 4096 </td><td align="center"> 8192 </td><td align="center"> 16384 </td><td align="center"> 32768 </td></tr><tr><td class="thead-hr" colspan="6"><hr /></td></tr><tr><td align="left"><i>K</i>NN </td><td align="center"> 0.623 </td><td align="center"> 1.389 </td><td align="center"> 5.721 </td><td align="center"> 22.254 </td><td align="center"> 86.706 </td></tr><tr><td align="left"><svg height="9.21094pt" id="M319" style="vertical-align:-0.2129908pt" version="1.1" viewbox="-0.0498162 -8.99795 13.3451 9.21094" width="13.3451pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><path d="M962 650H739L732 622L760 619C812 613 819 604 798 552C760 457 671 267 606 131H604L511 638H480L237 131H233L183 554C177 607 183 614 226 619L248 622L257 650H24L17 622C88 615 92 611 103 524L173 -11H203L450 491H453L543 -11H575L839 529C882 609 886 613 953 622L962 650Z" id="g113-88"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="974" vert-adv-y="974"></glyph.data></g></svg> in Isomap </td><td align="center"> 9.132 </td><td align="center"> 56.517 </td><td align="center"> 128.225 </td><td align="center"> 457.306 </td><td align="center"> 1697.124 </td></tr><tr><td align="left">Normalize </td><td align="center"> 0.160 </td><td align="center"> 0.905 </td><td align="center"> 6.526 </td><td align="center"> 223.240 </td><td align="center"> 2546.11 </td></tr><tr><td align="left">Eigensolve </td><td align="center"> 0.050 </td><td align="center"> 0.155 </td><td align="center"> 0.188 </td><td align="center"> 0.699 </td><td align="center"> 2.838 </td></tr><tr class="table-tr"><td colspan="6"><hr class="tbody-hr" /></td></tr></table></td></tr></table></div></div>

Table 4 | Parallel Framework for Dimensionality Reduction of Large-Scale Datasets

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Table 4: Parallel Framework for Dimensionality Reduction of Large-Scale Datasets

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Table 4

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