This paper presents a novel technique for the segmentation of data W = [w1 · · · wn] ⊂ R^D drawn from a union u = ∪^Mi=1 of subspaces {Si}^Mi=1. First, an existing subspace segmentation algorithm is used to perform an initial data clustering {Ci}^Mi=1, where Ci = {wi1 · · ·wik} ⊂ W is the set of data from the i^th cluster. Then, a local subspace LSi is matched for each Ci and the distance dij between LSi and each point wij ∊ Ci is computed. A data-driven threshold η is computed and the data points (in Ci) whose distances to LSi are larger than η are eliminated since they are considered as outliers or erroneously clustered data points in Ci. The remaining data points Ci ⊂ Ci are considered to be coming from the same subspace with high confidence. Then, {Ci}^Mi=1 are used in unsupervised way to train a convolution neural network to obtain a deep learning model, which is in turn used to re-cluster W. The system has been successfully implemented using the MNIST dataset and it improved the segmentation accuracy of a particular algorithm (EnSC-ORGEN) from 93.79% to 96.52%.