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CLASSIFICATION OF PHOTOGRAMMETRIC POINT CLOUDS OF SCAFFOLDS FOR CONSTRUCTION SITE MONITORING USING SUBSPACE CLUSTERING AND PCA
Scaffolding components Photogrammetric point clouds Subspace clustering PCA Classification
2016/7/28
This paper presents an approach for the classification of photogrammetric point clouds of scaffolding components in a construction site, aiming at making a preparation for the automatic monitoring of ...
HYPERSPECTRAL IMAGE KERNEL SPARSE SUBSPACE CLUSTERING WITH SPATIAL MAX POOLING OPERATION
Hyperspectral image nonlinear processing spatial max pooling SSC kernel
2016/7/28
In this paper, we present a kernel sparse subspace clustering with spatial max pooling operation (KSSC-SMP) algorithm for hyperspectral remote sensing imagery. Firstly, the feature points are mapped f...
A Geometric Analysis of Subspace Clustering with Outliers
Subspace clustering spectral clustering outlier detection `1 minimization duality in linear programming geometric functional analysis properties of convex bodies concentration of measure
2015/6/17
This paper considers the problem of clustering a collection of unlabeled data points assumed to lie near a union of lower dimensional planes. As is common in computer vision or unsupervised learning a...
Robust Subspace Clustering
Subspace clustering spectral clustering LASSO Dantzig selector `1 minimization multiple hypothesis testing true and false discoveries geometric functional analysis nonasymptotic random matrix theory
2015/6/17
Subspace clustering refers to the task of finding a multi-subspace representation that best fits a collection of points taken from a high-dimensional space. This paper introduces an algorithm inspired...
We consider the problem of clustering noisy high-dimensional data points into a union of low-dimensional subspaces and a set of outliers. The number of subspaces, their dimensions, and their orientati...
Greedy Feature Selection for Subspace Clustering
Subspace clustering unions of subspaces hybrid linear models sparse ap-proximation structured sparsity nearest neighbors low-rank approximation
2013/5/2
Unions of subspaces are powerful nonlinear signal models for collections of high-dimensional data. However, existing methods that exploit this structure require that the subspaces the signals of inter...
Subspace Clustering via Thresholding and Spectral Clustering
Subspace Clustering Thresholding Spectral Clustering
2013/5/2
We consider the problem of clustering a set of high-dimensional data points into sets of low-dimensional linear subspaces. The number of subspaces, their dimensions, and their orientations are unknown...
Model-based subspace clustering
COSA Dirichlet process mixture model nonparametric Bayes unsupervised learning variable selection
2009/9/21
We discuss a model-based approach to identifying clusters of objects
based on subsets of attributes, so that the attributes that distinguish a cluster
from the rest of the population may depend on t...