Matches in SemOpenAlex for { <https://semopenalex.org/work/W2897973071> ?p ?o ?g. }
- W2897973071 endingPage "1546" @default.
- W2897973071 startingPage "1534" @default.
- W2897973071 abstract "The problem of organizing data that evolves over time into clusters is encountered in a number of practical settings. We introduce evolutionary subspace clustering, a method whose objective is to cluster a collection of evolving data points that lie on a union of low-dimensional evolving subspaces. To learn the parsimonious representation of the data points at each time step, we propose a non-convex optimization framework that exploits the self-expressiveness property of the evolving data while taking into account representation from the preceding time step. To find an approximate solution to the aforementioned non-convex optimization problem, we develop a scheme based on alternating minimization that both learns the parsimonious representation as well as adaptively tunes and infers a smoothing parameter reflective of the rate of data evolution. The latter addresses a fundamental challenge in evolutionary clustering—determining if and to what extent one should consider previous clustering solutions when analyzing an evolving data collection. Our experiments on both synthetic and real-world datasets demonstrate that the proposed framework outperforms state-of-the-art static subspace clustering algorithms and existing evolutionary clustering schemes in terms of both accuracy and running time, in a range of scenarios." @default.
- W2897973071 created "2018-10-26" @default.
- W2897973071 creator A5036900440 @default.
- W2897973071 creator A5067602750 @default.
- W2897973071 date "2018-12-01" @default.
- W2897973071 modified "2023-09-27" @default.
- W2897973071 title "Evolutionary Self-Expressive Models for Subspace Clustering" @default.
- W2897973071 cites W1604980173 @default.
- W2897973071 cites W1680189815 @default.
- W2897973071 cites W1890623278 @default.
- W2897973071 cites W1981458038 @default.
- W2897973071 cites W1993962865 @default.
- W2897973071 cites W1995168330 @default.
- W2897973071 cites W1997201895 @default.
- W2897973071 cites W1998819761 @default.
- W2897973071 cites W2002260165 @default.
- W2897973071 cites W2002329661 @default.
- W2897973071 cites W2003217181 @default.
- W2897973071 cites W2003610960 @default.
- W2897973071 cites W2022651954 @default.
- W2897973071 cites W2037549374 @default.
- W2897973071 cites W2052311585 @default.
- W2897973071 cites W2056639756 @default.
- W2897973071 cites W2071631699 @default.
- W2897973071 cites W2076261573 @default.
- W2897973071 cites W2078204800 @default.
- W2897973071 cites W2079558799 @default.
- W2897973071 cites W2096608935 @default.
- W2897973071 cites W2102380305 @default.
- W2897973071 cites W2104990759 @default.
- W2897973071 cites W2116367120 @default.
- W2897973071 cites W2120669055 @default.
- W2897973071 cites W2125742596 @default.
- W2897973071 cites W2128659236 @default.
- W2897973071 cites W2133515443 @default.
- W2897973071 cites W2135046866 @default.
- W2897973071 cites W2138835141 @default.
- W2897973071 cites W2139054653 @default.
- W2897973071 cites W2145962650 @default.
- W2897973071 cites W2145977038 @default.
- W2897973071 cites W2150000644 @default.
- W2897973071 cites W2152714163 @default.
- W2897973071 cites W2158249175 @default.
- W2897973071 cites W2170337404 @default.
- W2897973071 cites W2185081213 @default.
- W2897973071 cites W2199534117 @default.
- W2897973071 cites W2222512263 @default.
- W2897973071 cites W2293546752 @default.
- W2897973071 cites W2527399347 @default.
- W2897973071 cites W2532206188 @default.
- W2897973071 cites W2561426102 @default.
- W2897973071 cites W258053484 @default.
- W2897973071 cites W2609983914 @default.
- W2897973071 cites W2713961636 @default.
- W2897973071 cites W272277767 @default.
- W2897973071 cites W2747622561 @default.
- W2897973071 cites W2805651580 @default.
- W2897973071 cites W2893821991 @default.
- W2897973071 cites W2949483514 @default.
- W2897973071 cites W2963072183 @default.
- W2897973071 cites W2963098825 @default.
- W2897973071 cites W2963840432 @default.
- W2897973071 cites W3022380717 @default.
- W2897973071 cites W3099880660 @default.
- W2897973071 cites W3105175345 @default.
- W2897973071 cites W3105745294 @default.
- W2897973071 cites W4235599520 @default.
- W2897973071 cites W4250657332 @default.
- W2897973071 cites W4250955649 @default.
- W2897973071 cites W4292363360 @default.
- W2897973071 cites W2886985401 @default.
- W2897973071 doi "https://doi.org/10.1109/jstsp.2018.2877478" @default.
- W2897973071 hasPublicationYear "2018" @default.
- W2897973071 type Work @default.
- W2897973071 sameAs 2897973071 @default.
- W2897973071 citedByCount "12" @default.
- W2897973071 countsByYear W28979730712019 @default.
- W2897973071 countsByYear W28979730712021 @default.
- W2897973071 countsByYear W28979730712022 @default.
- W2897973071 countsByYear W28979730712023 @default.
- W2897973071 crossrefType "journal-article" @default.
- W2897973071 hasAuthorship W2897973071A5036900440 @default.
- W2897973071 hasAuthorship W2897973071A5067602750 @default.
- W2897973071 hasBestOaLocation W28979730711 @default.
- W2897973071 hasConcept C12362212 @default.
- W2897973071 hasConcept C124101348 @default.
- W2897973071 hasConcept C154945302 @default.
- W2897973071 hasConcept C159149176 @default.
- W2897973071 hasConcept C17744445 @default.
- W2897973071 hasConcept C184509293 @default.
- W2897973071 hasConcept C199539241 @default.
- W2897973071 hasConcept C21080849 @default.
- W2897973071 hasConcept C2524010 @default.
- W2897973071 hasConcept C2776359362 @default.
- W2897973071 hasConcept C31972630 @default.
- W2897973071 hasConcept C32834561 @default.
- W2897973071 hasConcept C33923547 @default.
- W2897973071 hasConcept C3770464 @default.