2 research outputs found
Constructing the F-Graph with a Symmetric Constraint for Subspace Clustering
Based on further studying the low-rank subspace clustering (LRSC) and
L2-graph subspace clustering algorithms, we propose a F-graph subspace
clustering algorithm with a symmetric constraint (FSSC), which constructs a new
objective function with a symmetric constraint basing on F-norm, whose the most
significant advantage is to obtain a closed-form solution of the coefficient
matrix. Then, take the absolute value of each element of the coefficient
matrix, and retain the k largest coefficients per column, set the other
elements to 0, to get a new coefficient matrix. Finally, FSSC performs spectral
clustering over the new coefficient matrix. The experimental results on face
clustering and motion segmentation show FSSC algorithm can not only obviously
reduce the running time, but also achieve higher accuracy compared with the
state-of-the-art representation-based subspace clustering algorithms, which
verifies that the FSSC algorithm is efficacious and feasible.Comment: 9 pages, 1 figur
Research on Clustering Performance of Sparse Subspace Clustering
Recently, sparse subspace clustering has been a valid tool to deal with
high-dimensional data. There are two essential steps in the framework of sparse
subspace clustering. One is solving the coefficient matrix of data, and the
other is constructing the affinity matrix from the coefficient matrix, which is
applied to the spectral clustering. This paper investigates the factors which
affect clustering performance from both clustering accuracy and stability of
the approaches based on existing algorithms. We select four methods to solve
the coefficient matrix and use four different ways to construct a similarity
matrix for each coefficient matrix. Then we compare the clustering performance
of different combinations on three datasets. The experimental results indicate
that both the coefficient matrix and affinity matrix have a huge influence on
clustering performance and how to develop a stable and valid algorithm still
needs to be studied.Comment: 12 pages, 2 figure