Graph Sparse Nonnegative Matrix Factorization Algorithm Based on the Inertial Projection Neural Network

<div>Iteration number versus objective value for GSNMF when <span class="nowrap"><svg height="9.49473pt" id="M264" style="vertical-align:-0.2063999pt" version="1.1" viewbox="-0.0498162 -9.28833 28.4893 9.49473" width="28.4893pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M529 97L508 118C475 75 449 58 438 58C428 58 421 66 415 104C393 234 374 403 364 496C345 670 307 712 254 712C220 712 174 691 153 669L161 645C176 653 194 658 206 658C237 658 261 640 278 562C287 522 290 483 293 434C223 269 110 105 23 9L32 -12C59 -6 85 0 108 7C152 64 251 252 300 366C307 297 315 221 337 82C346 24 363 -12 393 -12C425 -12 475 13 529 97Z" id="g113-233"></path></g><g transform="matrix(.013,0,0,-0.013,10.806,0)"><path d="M535 323V373H52V323H535ZM535 138V188H52V138H535Z" id="g117-34"></path></g><g transform="matrix(.013,0,0,-0.013,22.068,0)"><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></g></svg>.</span></div>

Complexity

fig1

Figure 1

Figure 1: Graph Sparse Nonnegative Matrix Factorization Algorithm Based on the Inertial Projection Neural Network 