scholarly journals A tensor compression algorithm using Tucker decomposition and dictionary dimensionality reduction

2020 ◽  
Vol 16 (4) ◽  
pp. 155014772091640
Author(s):  
Chenquan Gan ◽  
Junwei Mao ◽  
Zufan Zhang ◽  
Qingyi Zhu
Keyword(s):  
Dimensionality Reduction ◽  
Sparse Representation ◽  
Original Data ◽  
Compression Algorithm ◽  
Tucker Decomposition ◽  
Compression Algorithms ◽  
Multidimensional Signals ◽  
Sparse Coefficient ◽  
Tensor Data ◽  
The Relationship

Tensor compression algorithms play an important role in the processing of multidimensional signals. In previous work, tensor data structures are usually destroyed by vectorization operations, resulting in information loss and new noise. To this end, this article proposes a tensor compression algorithm using Tucker decomposition and dictionary dimensionality reduction, which mainly includes three parts: tensor dictionary representation, dictionary preprocessing, and dictionary update. Specifically, the tensor is respectively performed by the sparse representation and Tucker decomposition, from which one can obtain the dictionary, sparse coefficient, and core tensor. Furthermore, the sparse representation can be obtained through the relationship between sparse coefficient and core tensor. In addition, the dimensionality of the input tensor is reduced by using the concentrated dictionary learning. Finally, some experiments show that, compared with other algorithms, the proposed algorithm has obvious advantages in preserving the original data information and denoising ability.

scholarly journals Dimensionality Reduction with Sparse Locality for Principal Component Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Pei Heng Li ◽  
Taeho Lee ◽  
Hee Yong Youn
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
Sparse Representation ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Reconstruction Error ◽  
Dimensional Representation ◽  
Weighted Matrix ◽  
Low Dimensional

Various dimensionality reduction (DR) schemes have been developed for projecting high-dimensional data into low-dimensional representation. The existing schemes usually preserve either only the global structure or local structure of the original data, but not both. To resolve this issue, a scheme called sparse locality for principal component analysis (SLPCA) is proposed. In order to effectively consider the trade-off between the complexity and efficiency, a robust L2,p-norm-based principal component analysis (R2P-PCA) is introduced for global DR, while sparse representation-based locality preserving projection (SR-LPP) is used for local DR. Sparse representation is also employed to construct the weighted matrix of the samples. Being parameter-free, this allows the construction of an intrinsic graph more robust against the noise. In addition, simultaneous learning of projection matrix and sparse similarity matrix is possible. Experimental results demonstrate that the proposed scheme consistently outperforms the existing schemes in terms of clustering accuracy and data reconstruction error.

scholarly journals Nonverbal Semantics Test (NVST)—A Novel Diagnostic Tool to Assess Semantic Processing Deficits: Application to Persons with Aphasia after Cerebrovascular Accident

2021 ◽  
Vol 11 (3) ◽  
pp. 359
Author(s):  
Katharina Hogrefe ◽  
Georg Goldenberg ◽  
Ralf Glindemann ◽  
Madleen Klonowski ◽  
Wolfram Ziegler
Keyword(s):  
Language Processing ◽  
Cerebrovascular Accident ◽  
Semantic Processing ◽  
Factor Model ◽  
Principal Component ◽  
Original Data ◽  
Verbal Abilities ◽  
Verbal Tasks ◽  
Alternative Means ◽  
The Relationship

Assessment of semantic processing capacities often relies on verbal tasks which are, however, sensitive to impairments at several language processing levels. Especially for persons with aphasia there is a strong need for a tool that measures semantic processing skills independent of verbal abilities. Furthermore, in order to assess a patient’s potential for using alternative means of communication in cases of severe aphasia, semantic processing should be assessed in different nonverbal conditions. The Nonverbal Semantics Test (NVST) is a tool that captures semantic processing capacities through three tasks—Semantic Sorting, Drawing, and Pantomime. The main aim of the current study was to investigate the relationship between the NVST and measures of standard neurolinguistic assessment. Fifty-one persons with aphasia caused by left hemisphere brain damage were administered the NVST as well as the Aachen Aphasia Test (AAT). A principal component analysis (PCA) was conducted across all AAT and NVST subtests. The analysis resulted in a two-factor model that captured 69% of the variance of the original data, with all linguistic tasks loading high on one factor and the NVST subtests loading high on the other. These findings suggest that nonverbal tasks assessing semantic processing capacities should be administered alongside standard neurolinguistic aphasia tests.

scholarly journals Pansharpening of WorldView-2 Data via Graph Regularized Sparse Coding and Adaptive Coupled Dictionary

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3586
Author(s):  
Wenqing Wang ◽  
Han Liu ◽  
Guo Xie
Keyword(s):  
High Resolution ◽  
Sparse Representation ◽  
Sparse Coding ◽  
Spectral Response ◽  
Objective Evaluation ◽  
Coefficient Matrix ◽  
Image Patch ◽  
Subjective Analysis ◽  
High Resolution Image ◽  
Sparse Coefficient

The spectral mismatch between a multispectral (MS) image and its corresponding panchromatic (PAN) image affects the pansharpening quality, especially for WorldView-2 data. To handle this problem, a pansharpening method based on graph regularized sparse coding (GRSC) and adaptive coupled dictionary is proposed in this paper. Firstly, the pansharpening process is divided into three tasks according to the degree of correlation among the MS and PAN channels and the relative spectral response of WorldView-2 sensor. Then, for each task, the image patch set from the MS channels is clustered into several subsets, and the sparse representation of each subset is estimated through the GRSC algorithm. Besides, an adaptive coupled dictionary pair for each task is constructed to effectively represent the subsets. Finally, the high-resolution image subsets for each task are obtained by multiplying the estimated sparse coefficient matrix by the corresponding dictionary. A variety of experiments are conducted on the WorldView-2 data, and the experimental results demonstrate that the proposed method achieves better performance than the existing pansharpening algorithms in both subjective analysis and objective evaluation.

Semi-supervised non-negative Tucker decomposition for tensor data representation

2021 ◽  
Author(s):  
YuNing Qiu ◽  
GuoXu Zhou ◽  
XinQi Chen ◽  
DongPing Zhang ◽  
XinHai Zhao ◽  
...  
Keyword(s):  
Data Representation ◽  
Tucker Decomposition ◽  
Tensor Data

MARGINALIZED YET MOBILIZED

2019 ◽  
Vol 16 (1) ◽  
pp. 131-156 ◽  
Author(s):  
Alecia J. McGregor ◽  
Laura M. Bogart ◽  
Molly Higgins-Biddle ◽  
Dara Z. Strolovitch ◽  
Bisola Ojikutu
Keyword(s):  
Sexual Behavior ◽  
Political Participation ◽  
Original Data ◽  
Preliminary Evidence ◽  
Same Sex ◽  
Electoral Outcomes ◽  
Marginalized Groups ◽  
Nationally Representative ◽  
Lgbt People ◽  
The Relationship

AbstractBoth African American and LGBT voters can prove pivotal in electoral outcomes, but we know little about civic participation among Black LGBT people. Although decades of research on political participation has made it almost an article of faith that members of dominant groups (such as White people and individuals of higher socioeconomic status) vote at higher rates than their less privileged counterparts, recent work has suggested that there are circumstances under which members of marginalized groups might participate at higher rates. Some of this research suggests that political participation might also increase when groups perceive elections as particularly threatening. We argue that when such threats are faced by marginalized groups, the concern to protect hard-earned rights can activate a sense of what we call “political hypervigilance,” and that such effects may be particularly pronounced among members of intersectionally-marginalized groups such as LGBT African Americans. To test this theory, we use original data from the 2016 National Survey on HIV in the Black Community, a nationally-representative survey of Black Americans, to explore the relationship among same-sex sexual behavior, attitudes toward LGBT people, and respondent voting intentions in the 2016 presidential election. We find that respondents who reported having engaged in same-sex sexual behavior were strongly and significantly more likely to say they “definitely will vote” compared to respondents who reported no same-sex sexual behavior. More favorable views of LGBT individuals and issues (marriage equality) were also associated with greater intention to vote. We argue that these high rates provide preliminary evidence that political hypervigilance can, in fact, lead to increased political engagement among members of marginalized groups.

Entrenchment in status positions and the adoption of new norm-deviant organizational practices: Evidence from the Japanese banking industry, 1983–2005

2020 ◽  
pp. 017084062091095
Author(s):  
Jesper Edman ◽  
Alex Makarevich
Keyword(s):  
Banking Industry ◽  
Original Data ◽  
Organizational Practices ◽  
Organizational Status ◽  
Factors Affecting ◽  
Practice Adoption ◽  
Status Mobility ◽  
The Relationship ◽  
Organizational Age ◽  
Middle Status

We examine the effect of status entrenchment on the adoption of new norm-deviant organizational practices. Identifying organizational age and status mobility as factors affecting entrenchment, we extend the middle-status conformity theory by explicating how entrenchment moderates the relationship between status and adoption. Using original data from the Japanese loan syndication market, we show that young and new-in-status banks have a lower propensity to follow status-based adoption behavior than actors entrenched in the same status positions. We discuss implication of these results for the understanding of new practice adoption and organizational status effects.

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