Nearest q-Flat to m Points

By Paul Tseng. 2000

221 citations recorded by the source corpus.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c69f6914f15d9c42c835b3eca5018acf7898aac1

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In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

Citations in the corpus

Robust recovery of multiple subspaces by geometric l_p minimization (2011, methodology): “TheK-subspaces algorithm [5, 10, 22] is the most basic heuristic for HLM, and it suggests an iterative procedure attempting to minimize the energy (1) with p= 2.”
Spectral Curvature Clustering (SCC) (2009, methodology): “There are also algorithms that iterate between the two components of data clustering and subspace modeling (see e.g., Mixtures of PPCA (MoPPCA) (Tipping and Bishop 1999), K-Subspaces (Ho et al. 2003)/k-Planes (Bradley and Mangasarian 2000; Tseng 1999 )).”
Generalized Low Rank Models (2014, methodology): “This method is sometimes called the k-planes algorithm [150, 146, 3], which alternates over assigning examples to subspaces, and fitting the subspaces to the examples.”
Sparse Subspace Clustering: Algorithm, Theory, and Applications (2013, methodology): “Iterative approaches, such as Ksubspaces [12], [13] and median K-flats [14], alternate between assigning points to subspaces and fitting a subspace to each cluster.”
Multi-view Subspace Clustering (2015, methodology): “For instance, the iteration based methods such as [21] and [11], the factorization based methods such as [5] and [12], statistical approaches such as [20],”
Subspace Clustering by Block Diagonal Representation (2018, methodology): “Representative methods are K-plane [3] and Q-flat [36].”
Robust Subspace Clustering (2013, methodology): “A representative example of an iterative method—the term is taken from the tutorial [53]—is the K-subspace algorithm [52], a procedure which can be viewed as a generalization of K-means.”
Low rank subspace clustering (LRSC) (2014, methodology): “In the case of data corrupted by gross errors, we solve the problem using an alternating minimization approach, which combines our polynomial thresholding operator with the more traditional shrinkage-thresholding operator.”
Subspace Clustering (2011, methodology): “The K-subspaces algorithm [12], [13] further generalizes K-planes from multiple hyperplanes to multiple affine subspaces of any dimensions and proceeds as follows.”
A Geometric Analysis of Subspace Clustering with Outliers (2011, background): “From now on, it will be convenient to arrange the observed data points as columns of a matrix X = [ x 1 , . . . , x N ] ∈ R n × N , where N = N 0 + N 1 + . . . + N L is the total number of points.”
Classification and clustering via dictionary learning with structured incoherence and shared features (2010, background): “This problem is closely related with the k-q-flat algorithm that aims at finding the closest k q-dimensional flats to a dataset [30].”
Sparse Subspace Clustering: Algorithm, Theory, and Applications (2012, methodology): “Iterative approaches, such as Ksubspaces [12], [13] and median K-flats [14], alternate between assigning points to subspaces and fitting a subspace to each cluster.”