As　a　typical　non-Gaussian　vector　variable,　a　neutral　vector　variable　contains　nonnegative　elements　only,　and　its　$l_{1}$　-norm　equals　one.　In　addition,　its　neutral　properties　make　it　significantly　different　from　the　commonly　studied　vector　variables　(e.g.,　the　Gaussian　vector　variables).　Due　to　the　aforementioned　properties,　the　conventionally　applied　linear　transformation　approaches　[e.g.,　principal　component　analysis　(PCA)　and　independent　component　analysis　(ICA)]　are　not　suitable　for　neutral　vector　variables,　as　PCA　cannot　transform　a　neutral　vector　variable,　which　is　highly　negatively　correlated,　into　a　set　of　mutually　independent　scalar　variables　and　ICA　cannot　preserve　the　bounded　property　after　transformation.　In　recent　work,　we　proposed　an　efficient　nonlinear　transformation　approach,　i.e.,　the　parallel　nonlinear　transformation　(PNT),　for　decorrelating　neutral　vector　variables.　In　this　article,　we　extensively　compare　PNT　with　PCA　and　ICA　through　both　theoretical　analysis　and　experimental　evaluations.　The　results　of　our　investigations　demonstrate　the　superiority　of　PNT　for　decorrelating　the　neutral　vector　variables.