With　the　popularity　of　multimedia　technology,　information　is　always　represented　from　multiple　views.　Even　though　multiview　data　can　reflect　the　same　sample　from　different　perspectives,　multiple　views　are　consistent　to　some　extent　because　they　are　representations　of　the　same　sample.　Most　of　the　existing　algorithms　are　graph-based　ones　to　learn　the　complex　structures　within　multiview　data　but　overlook　the　information　within　data　representations.　Furthermore,　many　existing　works　treat　multiple　views　discriminatively　by　introducing　some　hyperparameters,　which　is　undesirable　in　practice.　To　this　end,　abundant　multiview-based　methods　have　been　proposed　for　dimension　reduction.　However,　there　is　still　no　research　that　leverages　the　existing　work　into　a　unified　framework.　In　this　paper,　we　propose　a　general　framework　for　multiview　data　dimension　reduction,　named　kernelized　multiview　subspace　analysis　(KMSA)　to　handle　multiview　feature　representation　in　the　kernel　space,　providing　a　feasible　channel　for　multiview　data　with　different　dimensions.　Compared　with　the　graph-based　methods,　KMSA　can　fully　exploit　information　from　multiview　data　with　nothing　to　lose.　Since　different　views　have　different　influences　on　KMSA,　we　propose　a　self-weighted　strategy　to　treat　different　views　discriminatively.　A　co-regularized　term　is　proposed　to　promote　the　mutual　learning　from　multiviews.　KMSA　combines　self-weighted　learning　with　the　co-regularized　term　to　learn　the　appropriate　weights　for　all　views.　We　evaluate　our　proposed　framework　on　6　multiview　datasets　for　classification　and　image　retrieval.　The　experimental　results　validate　the　advantages　of　our　proposed　method.