With　the　growing　interest　in　convex　and　nonconvex　low-rank　matrix　learning　problems,　the　widely　used　singular　value　thresholding　(SVT)　operators　associated　with　rank　relaxation　functions　often　face　higher　computational　complexity,　particularly　for　large-scale　data　matrices.　To　improve　the　efficacy　of　low-rank　subspace　clustering　and　overcome　the　issue　of　high　computational　complexity,　this　work　proposes　an　efficient　and　effective　method　that　avoids　the　need　for　singular　value　decomposition　(SVD)　computations　in　the　iteration　scheme.　This　can　be　achieved　through　the　use　of　a　computationally　efficient　and　compact　formulation,　as　well　as　automatic　removal　of　the　optimal　mean,　which　reduces　time　consumption　and　enhances　evaluation　performance.　A　unified　clustering　framework　based　on　Schatten-p　norm　regularized　by　ℓ2,q-norm　can　be　formulated　using　this　processing　way,　where　inner　element　suppression　can　be　achieved　by　choosing　appropriate　p,　q　∈　(0,1).　Additionally,　calculating　the　optimal　mean　enhances　the　robustness　of　the　proposed　method　in　the　presence　of　outliers.　Unlike　the　general　iteration　scheme　of　the　alternating　direction　method　of　multiplier　(ADMM)　algorithms　that　introduce　auxiliary　splitting　variables,　the　proposed　alternating　re-weighted　least　square　(ARwLS)　algorithm　uses　matrix　inverse　and　multiplication　computations　to　obtain　analytic　solutions,　resulting　in　faster　processing　speeds　for　each　sub-problem.　To　further　investigate,　we　provide　the　computational　complexity　of　each　iteration　and　the　theoretical　analysis　of　the　convergence　property,　where　the　derived　solution　is　a　stationary　point.　Experimental　results　on　synthetic　data　and　several　benchmark　datasets　demonstrate　the　promising　efficiency　and　efficacy　of　the　proposed　clustering　method　compared　to　classical　and　competing　algorithms.　IEEE