12 research outputs found
Efficient Algorithms for CUR and Interpolative Matrix Decompositions
The manuscript describes efficient algorithms for the computation of the CUR
and ID decompositions. The methods used are based on simple modifications to
the classical truncated pivoted QR decomposition, which means that highly
optimized library codes can be utilized for implementation. For certain
applications, further acceleration can be attained by incorporating techniques
based on randomized projections. Numerical experiments demonstrate advantageous
performance compared to existing techniques for computing CUR factorizations
CUR Decompositions, Similarity Matrices, and Subspace Clustering
A general framework for solving the subspace clustering problem using the CUR
decomposition is presented. The CUR decomposition provides a natural way to
construct similarity matrices for data that come from a union of unknown
subspaces . The similarity
matrices thus constructed give the exact clustering in the noise-free case.
Additionally, this decomposition gives rise to many distinct similarity
matrices from a given set of data, which allow enough flexibility to perform
accurate clustering of noisy data. We also show that two known methods for
subspace clustering can be derived from the CUR decomposition. An algorithm
based on the theoretical construction of similarity matrices is presented, and
experiments on synthetic and real data are presented to test the method.
Additionally, an adaptation of our CUR based similarity matrices is utilized
to provide a heuristic algorithm for subspace clustering; this algorithm yields
the best overall performance to date for clustering the Hopkins155 motion
segmentation dataset.Comment: Approximately 30 pages. Current version contains improved algorithm
and numerical experiments from the previous versio
A Parallel Hierarchical Blocked Adaptive Cross Approximation Algorithm
This paper presents a hierarchical low-rank decomposition algorithm assuming
any matrix element can be computed in time. The proposed algorithm
computes rank-revealing decompositions of sub-matrices with a blocked adaptive
cross approximation (BACA) algorithm, followed by a hierarchical merge
operation via truncated singular value decompositions (H-BACA). The proposed
algorithm significantly improves the convergence of the baseline ACA algorithm
and achieves reduced computational complexity compared to the full
decompositions such as rank-revealing QR decompositions. Numerical results
demonstrate the efficiency, accuracy and parallel efficiency of the proposed
algorithm
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Two accelerated isogeometric boundary element method formulations: fast multipole method and hierarchical matrices method
This work presents two fast isogeometric formulations of the Boundary Element Method (BEM) applied to heat conduction problems, one accelerated by Fast Multipole Method (FMM) and other by Hierarchical Matrices. The Fast Multipole Method uses complex variables and expansion of fundamental solutions into Laurant series, while the Hierarchical Matrices are created by low rank CUR approximations from the k−Means clustering technique for geometric sampling. Both use Non-Uniform Rational B-Splines (NURBS) as shape functions. To reduce computational cost and facilitate implementation, NURBS are decomposed into Bézier curves, making the isogeometric formulation very similar to the conventional BEM. A description of the hierarchical structure of the data and the implemented algorithms are presented. Validation is performed by comparing the results of the proposed formulations with those of the conventional BEM formulation. The computational cost of both formulations is analyzed showing the advantages of the proposed formulations for large scale problems
Randomized Matrix Decompositions Using R
Matrix decompositions are fundamental tools in the area of applied mathematics, statistical computing, and machine learning. In particular, low-rank matrix decompositions are vital, and widely used for data analysis, dimensionality reduction, and data compression. Massive datasets, however, pose a computational challenge for traditional algorithms, placing significant constraints on both memory and processing power. Recently, the powerful concept of randomness has been introduced as a strategy to ease the computational load. The essential idea of probabilistic algorithms is to employ some amount of randomness in order to derive a smaller matrix from a high-dimensional data matrix. The smaller matrix is then used to compute the desired low-rank approximation. Such algorithms are shown to be computationally efficient for approximating matrices with low-rank structure. We present the R package rsvd, and provide a tutorial introduction to randomized matrix decompositions. Specifically, randomized routines for the singular value decomposition, (robust) principal component analysis, interpolative decomposition, and CUR decomposition are discussed. Several examples demonstrate the routines, and show the computational advantage over other methods implemented in R