Unsupervised Deep Learning for Subspace Clustering

Koku, Ahmet Buğra
VANAMALA, Nagendrababu
This paper presents a novel technique for the segmentation of data W = [w(1) . . . w(N)] subset of R-D drawn from a union U = boolean OR(M)(i=1) S-i of subspaces {S-i}(i=1)(M). First, an existing subspace segmentation algorithm is used to perform an initial data clustering {C-i}(i=1)(M), where C-i = {w(i1) . . . w(ik)} subset of W is the set of data from the ith cluster. Then, a local subspace LSi is matched for each C-i and the distance d(ij) between LSi and each point w(ij) is an element of C-i is computed. A data-driven threshold eta is computed and the data points (in C-i) whose distances to LSi are larger than eta are eliminated since they are considered as outliers or erroneously clustered data points in C-i. The remaining data points (C) over tilde (i) subset of C-i are considered to be coming from the same subspace with high confidence. Then, {(C) over tilde (i)}(i=1)(M) 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%.


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Citation Formats
a. SEKMEN, A. B. Koku, M. PARLAKTUNA, A. ABDULMALEK, and N. VANAMALA, “Unsupervised Deep Learning for Subspace Clustering,” 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55782.