Column-wise Element Selection for Computationally Efficient Nonnegative Coupled Matrix Tensor Factorization

This study presents an effective approach to the hitherto little addressed problem of feature assessment and selection for pattern recognition in imprecisely supervised environments. Unlike in classical supervised environments wherein the representative training samples have crisp class labels, here the samples have fuzzy memberships in several of the different pattern classes in the environment. The new methodology reported here is an outgrowth of a recently developed tool CORPS—Class Overlap Region Partitioning Scheme initially designed for operation in supervised environments and extended later for operation in imperfectly supervised environments. The emphasis here has been the development of a computationally efficient scheme capable of evaluating as rapidly as practical a large number of features individually as to their discrimination potential based on which a smaller subset may be selected, if so desired, for more detailed evaluation in different combinations by other tools.

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Optimum reference element selection for GNSS power‐inversion adaptive arrays

Electronics Letters ◽

10.1049/el.2016.2360 ◽

2016 ◽

Vol 52 (20) ◽

pp. 1723-1725 ◽

Cited By ~ 9

Author(s):

Youda Wan ◽

Feiqiang Chen ◽

Junwei Nie ◽

Guangfu Sun

Keyword(s):

Adaptive Arrays ◽

Reference Element ◽

Selection For ◽

Power Inversion ◽

Element Selection

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Model Selection for Non-Negative Tensor Factorization with Minimum Description Length

Entropy ◽

10.3390/e21070632 ◽

2019 ◽

Vol 21 (7) ◽

pp. 632

Author(s):

Yunhui Fu ◽

Shin Matsushima ◽

Kenji Yamanishi

Keyword(s):

Missing Values ◽

Minimum Description Length ◽

Selection Criterion ◽

Real Data ◽

Tensor Factorization ◽

Trial And Error ◽

Negative Factor ◽

Mdl Principle ◽

Normalized Maximum Likelihood ◽

Selection For

Non-negative tensor factorization (NTF) is a widely used multi-way analysis approach that factorizes a high-order non-negative data tensor into several non-negative factor matrices. In NTF, the non-negative rank has to be predetermined to specify the model and it greatly influences the factorized matrices. However, its value is conventionally determined by specialists’ insights or trial and error. This paper proposes a novel rank selection criterion for NTF on the basis of the minimum description length (MDL) principle. Our methodology is unique in that (1) we apply the MDL principle on tensor slices to overcome a problem caused by the imbalance between the number of elements in a data tensor and that in factor matrices, and (2) we employ the normalized maximum likelihood (NML) code-length for histogram densities. We employ synthetic and real data to empirically demonstrate that our method outperforms other criteria in terms of accuracies for estimating true ranks and for completing missing values. We further show that our method can produce ranks suitable for knowledge discovery.

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Efficient Sampling Allocation Procedures for Optimal Quantile Selection

INFORMS Journal on Computing ◽

10.1287/ijoc.2019.0946 ◽

2020 ◽

Author(s):

Yijie Peng ◽

Chun-Hung Chen ◽

Michael C. Fu ◽

Jian-Qiang Hu ◽

Ilya O. Ryzhov

Keyword(s):

Sample Size ◽

Numerical Experiments ◽

Ranking And Selection ◽

Computationally Efficient ◽

Selection Procedures ◽

Efficient Sampling ◽

Selection For ◽

Dynamic Sampling ◽

Efficient Selection ◽

Sampling Allocation

We propose a dynamic sampling allocation and selection paradigm for finding the alternative with the optimal quantile in a Bayesian framework. Myopic allocation policies (MAPs), analogous to existing methods in classic ranking and selection for selecting the alternative with the optimal mean, and computationally efficient selection policies are derived for selecting the alternative with the optimal quantile. Under certain conditions, we prove that the proposed MAPs and selection procedures are consistent, which means that the best quantile would be eventually correctly selected as the sample size goes to infinity. Numerical experiments demonstrate that the proposed schemes can significantly improve the performance.

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