FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2971826129> ?p ?o ?g. }

• W2971826129 endingPage "1" @default.
• W2971826129 startingPage "1" @default.
• W2971826129 abstract "Linear discriminant analysis (LDA) is one of commonly used supervised subspace learning methods. However, LDA will be powerless faced with the no-label situation. In this paper, the unsupervised LDA (Un-LDA) is proposed and first formulated as a seamlessly unified objective optimization which guarantees convergence during the iteratively alternative solving process. The objective optimization is in both the ratio trace and the trace ratio forms, forming a complete framework of a new approach to jointly clustering and unsupervised subspace learning. The extension of LDA into Un-LDA enables to not only complete unsupervised subspace learning via the explicitly presented subspace projection matrix but also simultaneously finish clustering and even clustering out-of-sample data via the explicitly presented transformation matrix. To overcome the difficulty in solving the non-convex objective optimization, we mathematically prove that the Un-LDA optimization in both forms can be transformed into the simple K-means clustering optimization when the subspace is determined. The Un-LDA optimization is eventually completed by alternatively optimizing the clusters using K-means and the subspace using the supervised LDA methods and iterating this whole process until convergence or stopping criterion. The experiments demonstrate that our proposed Un-LDA algorithms are comparable or even much superior to the counterparts." @default.
• W2971826129 created "2019-09-12" @default.
• W2971826129 creator A5003222421 @default.
• W2971826129 creator A5019602351 @default.
• W2971826129 creator A5030351224 @default.
• W2971826129 creator A5054072550 @default.
• W2971826129 creator A5063131315 @default.
• W2971826129 creator A5086529957 @default.
• W2971826129 date "2019-01-01" @default.
• W2971826129 modified "2023-10-17" @default.
• W2971826129 title "Unsupervised Linear Discriminant Analysis for Jointly Clustering and Subspace Learning" @default.
• W2971826129 cites W1600471557 @default.
• W2971826129 cites W1894767581 @default.
• W2971826129 cites W19235576 @default.
• W2971826129 cites W1972908405 @default.
• W2971826129 cites W1992905164 @default.
• W2971826129 cites W1993962865 @default.
• W2971826129 cites W1997201895 @default.
• W2971826129 cites W2001619934 @default.
• W2971826129 cites W2003217181 @default.
• W2971826129 cites W2007453627 @default.
• W2971826129 cites W2015648984 @default.
• W2971826129 cites W2023717039 @default.
• W2971826129 cites W2028312814 @default.
• W2971826129 cites W2033478416 @default.
• W2971826129 cites W2037549374 @default.
• W2971826129 cites W2053186076 @default.
• W2971826129 cites W2054639298 @default.
• W2971826129 cites W2064210461 @default.
• W2971826129 cites W2085707296 @default.
• W2971826129 cites W2090579338 @default.
• W2971826129 cites W2098089028 @default.
• W2971826129 cites W2121647436 @default.
• W2971826129 cites W2121947440 @default.
• W2971826129 cites W2131828344 @default.
• W2971826129 cites W2132914434 @default.
• W2971826129 cites W2133607425 @default.
• W2971826129 cites W2146820706 @default.
• W2971826129 cites W2148214025 @default.
• W2971826129 cites W2151416140 @default.
• W2971826129 cites W2153320597 @default.
• W2971826129 cites W2155423555 @default.
• W2971826129 cites W2158281641 @default.
• W2971826129 cites W2169073444 @default.
• W2971826129 cites W2171898484 @default.
• W2971826129 cites W2197707282 @default.
• W2971826129 cites W2296609216 @default.
• W2971826129 cites W2299646912 @default.
• W2971826129 cites W2314735922 @default.
• W2971826129 cites W2345811097 @default.
• W2971826129 cites W2402014506 @default.
• W2971826129 cites W2427656690 @default.
• W2971826129 cites W2514638911 @default.
• W2971826129 cites W2532206188 @default.
• W2971826129 cites W2575552627 @default.
• W2971826129 cites W2588628126 @default.
• W2971826129 cites W2750378047 @default.
• W2971826129 cites W2783376202 @default.
• W2971826129 cites W2784522433 @default.
• W2971826129 cites W2795687440 @default.
• W2971826129 cites W2807548733 @default.
• W2971826129 cites W2883529134 @default.
• W2971826129 cites W2884277568 @default.
• W2971826129 cites W2884334719 @default.
• W2971826129 cites W2894278606 @default.
• W2971826129 cites W2963840432 @default.
• W2971826129 cites W4238240379 @default.
• W2971826129 cites W4250657332 @default.
• W2971826129 doi "https://doi.org/10.1109/tkde.2019.2939524" @default.
• W2971826129 hasPublicationYear "2019" @default.
• W2971826129 type Work @default.
• W2971826129 sameAs 2971826129 @default.
• W2971826129 citedByCount "16" @default.
• W2971826129 countsByYear W29718261292020 @default.
• W2971826129 countsByYear W29718261292021 @default.
• W2971826129 countsByYear W29718261292022 @default.
• W2971826129 countsByYear W29718261292023 @default.
• W2971826129 crossrefType "journal-article" @default.
• W2971826129 hasAuthorship W2971826129A5003222421 @default.
• W2971826129 hasAuthorship W2971826129A5019602351 @default.
• W2971826129 hasAuthorship W2971826129A5030351224 @default.
• W2971826129 hasAuthorship W2971826129A5054072550 @default.
• W2971826129 hasAuthorship W2971826129A5063131315 @default.
• W2971826129 hasAuthorship W2971826129A5086529957 @default.
• W2971826129 hasConcept C11413529 @default.
• W2971826129 hasConcept C137836250 @default.
• W2971826129 hasConcept C153180895 @default.
• W2971826129 hasConcept C154945302 @default.
• W2971826129 hasConcept C32834561 @default.
• W2971826129 hasConcept C33923547 @default.
• W2971826129 hasConcept C41008148 @default.
• W2971826129 hasConcept C69738355 @default.
• W2971826129 hasConcept C73555534 @default.
• W2971826129 hasConcept C8038995 @default.
• W2971826129 hasConceptScore W2971826129C11413529 @default.
• W2971826129 hasConceptScore W2971826129C137836250 @default.
• W2971826129 hasConceptScore W2971826129C153180895 @default.
• W2971826129 hasConceptScore W2971826129C154945302 @default.

• next