Sparse　coding　based　domain　adaptation　methods　aim　to　learn　a　robust　transfer　classifier　by　utilizing　the　knowledge　from　source　domain　and　the　learned　new　representation　of　both　domains.　Most　existing　works　have　achieved　remarkable　results　in　solving　linear　domain　shift　problems,　but　have　poor　performance　in　nonlinear　domain　shift　problems.　In　this　paper,　we　propose　a　Transfer　kernel　sparse　coding　based　on　dynamic　distribution　alignment　(TKSC-DDA)　approach　for　cross-domain　visual　recognition,　which　incorporates　dynamic　distributed　alignment　into　kernel　sparse　coding　to　learn　discriminative　and　robust　sparse　representations.　Extensive　experiment　on　visual　transfer　learning　tasks　demonstrate　that　our　proposed　method　can　significantly　out-perform　serval　state-of-the-art　approaches.　©　2024　IEEE.