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302
Low-Rank Tensors for Verbs in Compositional Distributional Semantics
"... Several compositional distributional se-mantic methods use tensors to model multi-way interactions between vectors. Unfortunately, the size of the tensors can make their use impractical in large-scale implementations. In this paper, we inves-tigate whether we can match the perfor-mance of full tenso ..."
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tensors with low-rank ap-proximations that use a fraction of the original number of parameters. We in-vestigate the effect of low-rank tensors on the transitive verb construction where the verb is a third-order tensor. The results show that, while the low-rank tensors re-quire about two orders
Low-rank Tensor Recovery via Iterative Hard
"... Abstract—We study recovery of low-rank tensors from a small number of measurements. A version of the iterative hard thresholding algorithm (TIHT) for the higher order singular value decomposition (HOSVD) is introduced. As a first step towards the analysis of the algorithm, we define a corresponding ..."
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Cited by 4 (1 self)
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Abstract—We study recovery of low-rank tensors from a small number of measurements. A version of the iterative hard thresholding algorithm (TIHT) for the higher order singular value decomposition (HOSVD) is introduced. As a first step towards the analysis of the algorithm, we define a corresponding
Low-Rank Tensor Approximations for Reliability Analysis
"... ABSTRACT: Low-rank tensor approximations have recently emerged as a promising tool for efficiently building surrogates of computational models with high-dimensional input. In this paper, we shed light on issues related to their construction with greedy approaches and demonstrate that meta-models bui ..."
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ABSTRACT: Low-rank tensor approximations have recently emerged as a promising tool for efficiently building surrogates of computational models with high-dimensional input. In this paper, we shed light on issues related to their construction with greedy approaches and demonstrate that meta
Low-Rank Tensors for Scoring Dependency Structures
"... Accurate scoring of syntactic structures such as head-modifier arcs in dependency parsing typically requires rich, high-dimensional feature representations. A small subset of such features is often se-lected manually. This is problematic when features lack clear linguistic meaning as in embeddings o ..."
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Cited by 19 (5 self)
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or when the information is blended across features. In this paper, we use tensors to map high-dimensional fea-ture vectors into low dimensional repre-sentations. We explicitly maintain the pa-rameters as a low-rank tensor to obtain low dimensional representations of words in their syntactic roles
Giannakis, “Nonparametric low-rank tensor imputation
- in Proc. IEEE Statistical Signal Process. Workshop, Ann Arbor
, 2012
"... Completion or imputation of three-way data arrays with missing en-tries is a basic problem encountered in various areas, including bio-informatics, image processing, and preference analysis. If available, prior information about the data at hand should be incorporated to enhance performance of the i ..."
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Cited by 2 (2 self)
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of the imputation method adopted. This is the motivation behind the proposed low-rank tensor estimator which leverages the correlation across slices of the data cube in the form of reproducing kernels. The rank of the tensor estimate is controlled by a novel regularization on the factors of its PARAFAC decompo
Imputation of Streaming Low-Rank Tensor Data
"... Abstract—Unraveling latent structure by means of multilinear models of tensor data is of paramount importance in timely inference tasks encountered with ‘Big Data ’ analytics. However, increasingly noisy, heterogeneous, and incomplete datasets as well as the need for real-time processing of streamin ..."
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of streaming data pose major challenges to this end. The present paper introduces a novel online (adaptive) algorithm to decompose low-rank tensors with missing entries, and perform imputation as a byproduct. The nov-el estimator minimizes an exponentially-weighted least-squares fitting error along with a
LOW-RANK TENSOR DECOMPOSITION BASED ANOMALY DETECTION FOR HYPERSPECTRAL IMAGERY
"... Anomaly detection becomes increasingly important in hyper-spectral image analysis, since it can now uncover many ma-terial substances which were previously unresolved by multi-spectral sensors. In this paper, we propose a Low-rank Tensor Decomposition based anomaly Detection (LTDD) algorithm for Hyp ..."
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Anomaly detection becomes increasingly important in hyper-spectral image analysis, since it can now uncover many ma-terial substances which were previously unresolved by multi-spectral sensors. In this paper, we propose a Low-rank Tensor Decomposition based anomaly Detection (LTDD) algorithm
APPROXIMATE RANK-DETECTING FACTORIZATION OF LOW-RANK TENSORS
"... We present an algorithm, AROFAC2, which detects the (CP-)rank of a degree 3 tensor and calculates its factorization into rank-one components. We provide generative conditions for the algorithm to work and demonstrate on both synthetic and real world data that AROFAC2 is a potentially outperform-ing ..."
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We present an algorithm, AROFAC2, which detects the (CP-)rank of a degree 3 tensor and calculates its factorization into rank-one components. We provide generative conditions for the algorithm to work and demonstrate on both synthetic and real world data that AROFAC2 is a potentially outperform-ing
Hierarchical low-rank tensors for multilingual transfer parsing
- In Conference on Empirical Methods in Natural Language Processing (EMNLP
, 2015
"... Accurate multilingual transfer parsing typ-ically relies on careful feature engineer-ing. In this paper, we propose a hierar-chical tensor-based approach for this task. This approach induces a compact feature representation by combining atomic fea-tures. However, unlike traditional tensor models, it ..."
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Cited by 1 (0 self)
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Accurate multilingual transfer parsing typ-ically relies on careful feature engineer-ing. In this paper, we propose a hierar-chical tensor-based approach for this task. This approach induces a compact feature representation by combining atomic fea-tures. However, unlike traditional tensor models
Results 1 - 10
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302
