This paper is concerned with ℓ q (0 < q < 1)-norm regularized minimization problems with a twice continuously differentiable loss function. For this class of nonconvex and nonsmooth composite problems, many algorithms have been proposed to solve them and most of which are of the first-order type. In this work, we propose a hybrid of proximal gradient method and subspace regularized Newton method, named HpgSRN. Unlike existing global convergent results for (regularized) Newton methods, the whole iterate sequence produced by HpgSRN is proved to have a finite length and converge to an L-type stationary point under a mild curve-ratio condition and the Kurdyka-Łojasiewicz property of the cost function, which does linearly if further a Kurdyka-Łojasiewicz property of exponent 1/2 holds. Moreover, a superlinear convergence rate for the iterate sequence is also achieved under an additional local error bound condition. Our convergence results do not require the isolatedness and strict local minimum properties of the L-stationary point. Numerical comparisons with ZeroFPR, a hybrid of proximal gradient method and quasi-Newton method for the forward-backward envelope of the cost function, proposed in [A.