3,092 research outputs found
Distributed Low-rank Subspace Segmentation
Vision problems ranging from image clustering to motion segmentation to
semi-supervised learning can naturally be framed as subspace segmentation
problems, in which one aims to recover multiple low-dimensional subspaces from
noisy and corrupted input data. Low-Rank Representation (LRR), a convex
formulation of the subspace segmentation problem, is provably and empirically
accurate on small problems but does not scale to the massive sizes of modern
vision datasets. Moreover, past work aimed at scaling up low-rank matrix
factorization is not applicable to LRR given its non-decomposable constraints.
In this work, we propose a novel divide-and-conquer algorithm for large-scale
subspace segmentation that can cope with LRR's non-decomposable constraints and
maintains LRR's strong recovery guarantees. This has immediate implications for
the scalability of subspace segmentation, which we demonstrate on a benchmark
face recognition dataset and in simulations. We then introduce novel
applications of LRR-based subspace segmentation to large-scale semi-supervised
learning for multimedia event detection, concept detection, and image tagging.
In each case, we obtain state-of-the-art results and order-of-magnitude speed
ups
Sketch-based subspace clustering of hyperspectral images
Sparse subspace clustering (SSC) techniques provide the state-of-the-art in clustering of hyperspectral images (HSIs). However, their computational complexity hinders their applicability to large-scale HSIs. In this paper, we propose a large-scale SSC-based method, which can effectively process large HSIs while also achieving improved clustering accuracy compared to the current SSC methods. We build our approach based on an emerging concept of sketched subspace clustering, which was to our knowledge not explored at all in hyperspectral imaging yet. Moreover, there are only scarce results on any large-scale SSC approaches for HSI. We show that a direct application of sketched SSC does not provide a satisfactory performance on HSIs but it does provide an excellent basis for an effective and elegant method that we build by extending this approach with a spatial prior and deriving the corresponding solver. In particular, a random matrix constructed by the Johnson-Lindenstrauss transform is first used to sketch the self-representation dictionary as a compact dictionary, which significantly reduces the number of sparse coefficients to be solved, thereby reducing the overall complexity. In order to alleviate the effect of noise and within-class spectral variations of HSIs, we employ a total variation constraint on the coefficient matrix, which accounts for the spatial dependencies among the neighbouring pixels. We derive an efficient solver for the resulting optimization problem, and we theoretically prove its convergence property under mild conditions. The experimental results on real HSIs show a notable improvement in comparison with the traditional SSC-based methods and the state-of-the-art methods for clustering of large-scale images
Kernel Truncated Regression Representation for Robust Subspace Clustering
Subspace clustering aims to group data points into multiple clusters of which
each corresponds to one subspace. Most existing subspace clustering approaches
assume that input data lie on linear subspaces. In practice, however, this
assumption usually does not hold. To achieve nonlinear subspace clustering, we
propose a novel method, called kernel truncated regression representation. Our
method consists of the following four steps: 1) projecting the input data into
a hidden space, where each data point can be linearly represented by other data
points; 2) calculating the linear representation coefficients of the data
representations in the hidden space; 3) truncating the trivial coefficients to
achieve robustness and block-diagonality; and 4) executing the graph cutting
operation on the coefficient matrix by solving a graph Laplacian problem. Our
method has the advantages of a closed-form solution and the capacity of
clustering data points that lie on nonlinear subspaces. The first advantage
makes our method efficient in handling large-scale datasets, and the second one
enables the proposed method to conquer the nonlinear subspace clustering
challenge. Extensive experiments on six benchmarks demonstrate the
effectiveness and the efficiency of the proposed method in comparison with
current state-of-the-art approaches.Comment: 14 page
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