327 research outputs found
The larger the better: Analysis of a scalable spectral clustering algorithm with cosine similarity
Chen (2018) proposed a scalable spectral clustering algorithm for cosine similarity to handle the task of clustering large data sets. It runs extremely fast, with a linear complexity in the size of the data, and achieves state of the art accuracy. This paper conducts perturbation analysis of the algorithm to understand the effect of discarding a perturbation term in an eigendecomposition step. Our results show that the accuracy of the approximation by the scalable algorithm depends on the connectivity of the clusters, their separation and sizes, and is especially accurate for large data sets
Guaranteed Sparse Signal Recovery with Highly Coherent Sensing Matrices
Compressive sensing is a methodology for the reconstruction of sparse or compressible signals using far fewer samples than required by the Nyquist criterion. However, many of the results in compressive sensing concern random sampling matrices such as Gaussian and Bernoulli matrices. In common physically feasible signal acquisition and reconstruction scenarios such as super-resolution of images, the sensing matrix has a non-random structure with highly correlated columns. Here we present a compressive sensing type recovery algorithm, called Partial Inversion (PartInv), that overcomes the correlations among the columns. We provide theoretical justification as well as empirical comparisons
Multiscale Geometric Methods for Data Sets II: Geometric Multi-Resolution Analysis
Data sets are often modeled as point clouds in , for large. It is
often assumed that the data has some interesting low-dimensional structure, for
example that of a -dimensional manifold , with much smaller than .
When is simply a linear subspace, one may exploit this assumption for
encoding efficiently the data by projecting onto a dictionary of vectors in
(for example found by SVD), at a cost for data points. When
is nonlinear, there are no "explicit" constructions of dictionaries that
achieve a similar efficiency: typically one uses either random dictionaries, or
dictionaries obtained by black-box optimization. In this paper we construct
data-dependent multi-scale dictionaries that aim at efficient encoding and
manipulating of the data. Their construction is fast, and so are the algorithms
that map data points to dictionary coefficients and vice versa. In addition,
data points are guaranteed to have a sparse representation in terms of the
dictionary. We think of dictionaries as the analogue of wavelets, but for
approximating point clouds rather than functions.Comment: Re-formatted using AMS styl
Foundations of a Multi-way Spectral Clustering Framework for Hybrid Linear Modeling
The problem of Hybrid Linear Modeling (HLM) is to model and segment data
using a mixture of affine subspaces. Different strategies have been proposed to
solve this problem, however, rigorous analysis justifying their performance is
missing. This paper suggests the Theoretical Spectral Curvature Clustering
(TSCC) algorithm for solving the HLM problem, and provides careful analysis to
justify it. The TSCC algorithm is practically a combination of Govindu's
multi-way spectral clustering framework (CVPR 2005) and Ng et al.'s spectral
clustering algorithm (NIPS 2001). The main result of this paper states that if
the given data is sampled from a mixture of distributions concentrated around
affine subspaces, then with high sampling probability the TSCC algorithm
segments well the different underlying clusters. The goodness of clustering
depends on the within-cluster errors, the between-clusters interaction, and a
tuning parameter applied by TSCC. The proof also provides new insights for the
analysis of Ng et al. (NIPS 2001).Comment: 40 pages. Minor changes to the previous version (mainly revised
Sections 2.2 & 2.3, and added references). Accepted to the Journal of
Foundations of Computational Mathematic
TransPose: 6D Object Pose Estimation with Geometry-Aware Transformer
Estimating the 6D object pose is an essential task in many applications. Due
to the lack of depth information, existing RGB-based methods are sensitive to
occlusion and illumination changes. How to extract and utilize the geometry
features in depth information is crucial to achieve accurate predictions. To
this end, we propose TransPose, a novel 6D pose framework that exploits
Transformer Encoder with geometry-aware module to develop better learning of
point cloud feature representations. Specifically, we first uniformly sample
point cloud and extract local geometry features with the designed local feature
extractor base on graph convolution network. To improve robustness to
occlusion, we adopt Transformer to perform the exchange of global information,
making each local feature contains global information. Finally, we introduce
geometry-aware module in Transformer Encoder, which to form an effective
constrain for point cloud feature learning and makes the global information
exchange more tightly coupled with point cloud tasks. Extensive experiments
indicate the effectiveness of TransPose, our pose estimation pipeline achieves
competitive results on three benchmark datasets.Comment: 10 pages, 5 figures, IEEE Journa
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