The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
View-specific neighborhoods commonly contain class-inconsistent neighbors in graph-based multi-view learning. A key problem is to handle class-inconsistent neighbors under each view. This paper employs jointly sparse learning to filter unreliable neighbors in the union of view-specific neighborhoods, via representing each entity in a weighted sum of its neighbors under each view. The proposed jointly...
A novel method is proposed for recovering low-rank component and sparsity component of noisy observations, using a local convex envelope of the matrix cardinality function over a local box. Two local relaxation models combined with implicit or explicit rank restriction are proposed for solving the rank-sparsity factorization. An iterative approach of the local relaxation and a post-processing refinement...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.