Matches in SemOpenAlex for { <https://semopenalex.org/work/W4213058999> ?p ?o ?g. }
- W4213058999 endingPage "426" @default.
- W4213058999 startingPage "426" @default.
- W4213058999 abstract "This paper considers the problem of adaptive estimation of graph signals under the impulsive noise environment. The existing least mean squares (LMS) approach suffers from severe performance degradation under an impulsive environment that widely occurs in various practical applications. We present a novel adaptive estimation over graphs based on Welsch loss (WL-G) to handle the problems related to impulsive interference. The proposed WL-G algorithm can efficiently reconstruct graph signals from the observations with impulsive noises by formulating the reconstruction problem as an optimization based on Welsch loss. An analysis on the performance of the WL-G is presented to develop effective sampling strategies for graph signals. A novel graph sampling approach is also proposed and used in conjunction with the WL-G to tackle the time-varying case. The performance advantages of the proposed WL-G over the existing LMS regarding graph signal reconstruction under impulsive noise environment are demonstrated." @default.
- W4213058999 created "2022-02-24" @default.
- W4213058999 creator A5015599289 @default.
- W4213058999 creator A5038734971 @default.
- W4213058999 date "2022-02-21" @default.
- W4213058999 modified "2023-09-25" @default.
- W4213058999 title "Robust Adaptive Estimation of Graph Signals Based on Welsch Loss" @default.
- W4213058999 cites W1513335440 @default.
- W4213058999 cites W1690143098 @default.
- W4213058999 cites W1698699930 @default.
- W4213058999 cites W1701760339 @default.
- W4213058999 cites W1937201840 @default.
- W4213058999 cites W1991252559 @default.
- W4213058999 cites W1995439741 @default.
- W4213058999 cites W1995589132 @default.
- W4213058999 cites W2010535094 @default.
- W4213058999 cites W2024457004 @default.
- W4213058999 cites W2030643321 @default.
- W4213058999 cites W2034112085 @default.
- W4213058999 cites W2072951627 @default.
- W4213058999 cites W2091734247 @default.
- W4213058999 cites W2099398754 @default.
- W4213058999 cites W2100556411 @default.
- W4213058999 cites W2101491865 @default.
- W4213058999 cites W2103158338 @default.
- W4213058999 cites W2103286460 @default.
- W4213058999 cites W2135160607 @default.
- W4213058999 cites W2144799407 @default.
- W4213058999 cites W2150709895 @default.
- W4213058999 cites W2152987686 @default.
- W4213058999 cites W2161041254 @default.
- W4213058999 cites W2161763921 @default.
- W4213058999 cites W2215668387 @default.
- W4213058999 cites W2259160646 @default.
- W4213058999 cites W2330981327 @default.
- W4213058999 cites W2535123509 @default.
- W4213058999 cites W2600434153 @default.
- W4213058999 cites W2606654184 @default.
- W4213058999 cites W2626271247 @default.
- W4213058999 cites W2734408317 @default.
- W4213058999 cites W2755388561 @default.
- W4213058999 cites W2796431263 @default.
- W4213058999 cites W2896675734 @default.
- W4213058999 cites W2897453016 @default.
- W4213058999 cites W2912349489 @default.
- W4213058999 cites W2949959605 @default.
- W4213058999 cites W2952791818 @default.
- W4213058999 cites W2962759781 @default.
- W4213058999 cites W2962817598 @default.
- W4213058999 cites W2963134661 @default.
- W4213058999 cites W2964083363 @default.
- W4213058999 cites W2965301612 @default.
- W4213058999 cites W2979750740 @default.
- W4213058999 cites W2982201391 @default.
- W4213058999 cites W3009604719 @default.
- W4213058999 cites W3010338607 @default.
- W4213058999 cites W3010884296 @default.
- W4213058999 cites W3011885421 @default.
- W4213058999 cites W3014507393 @default.
- W4213058999 cites W3017988012 @default.
- W4213058999 cites W3024853581 @default.
- W4213058999 cites W3032985158 @default.
- W4213058999 cites W3033485379 @default.
- W4213058999 cites W3036074714 @default.
- W4213058999 cites W3042158248 @default.
- W4213058999 cites W3049384921 @default.
- W4213058999 cites W3098927376 @default.
- W4213058999 cites W3109757238 @default.
- W4213058999 cites W3123456921 @default.
- W4213058999 cites W3135258945 @default.
- W4213058999 cites W3138044305 @default.
- W4213058999 cites W3195962189 @default.
- W4213058999 cites W3198728162 @default.
- W4213058999 cites W3205299117 @default.
- W4213058999 cites W3206707702 @default.
- W4213058999 cites W3210977953 @default.
- W4213058999 cites W3211299233 @default.
- W4213058999 doi "https://doi.org/10.3390/sym14020426" @default.
- W4213058999 hasPublicationYear "2022" @default.
- W4213058999 type Work @default.
- W4213058999 citedByCount "0" @default.
- W4213058999 crossrefType "journal-article" @default.
- W4213058999 hasAuthorship W4213058999A5015599289 @default.
- W4213058999 hasAuthorship W4213058999A5038734971 @default.
- W4213058999 hasBestOaLocation W42130589991 @default.
- W4213058999 hasConcept C11413529 @default.
- W4213058999 hasConcept C126255220 @default.
- W4213058999 hasConcept C132525143 @default.
- W4213058999 hasConcept C154945302 @default.
- W4213058999 hasConcept C33923547 @default.
- W4213058999 hasConcept C41008148 @default.
- W4213058999 hasConcept C80444323 @default.
- W4213058999 hasConceptScore W4213058999C11413529 @default.
- W4213058999 hasConceptScore W4213058999C126255220 @default.
- W4213058999 hasConceptScore W4213058999C132525143 @default.
- W4213058999 hasConceptScore W4213058999C154945302 @default.
- W4213058999 hasConceptScore W4213058999C33923547 @default.
- W4213058999 hasConceptScore W4213058999C41008148 @default.