Jocelyn Chi

Works (5)

Updated: July 5th, 2023 15:39

2021 article

A projector-based approach to quantifying total and excess uncertainties for sketched linear regression

Chi, J. T., & Ipsen, I. C. F. (2021, June 18). Information and Inference A Journal of the IMA, Vol. 8.

By: J. Chi n & I. Ipsen n 

author keywords: expectation; variance; bias; mean squared error; predictive risk
topics (OpenAlex): Stochastic Gradient Optimization Techniques; Topological and Geometric Data Analysis; Data Visualization and Analytics; Statistical Methods and Inference; Statistical Methods and Bayesian Inference
TL;DR: A projector-based approach to sketched linear regression is presented that is exact and that requires minimal assumptions on the sketching matrix, and enables derivation of key quantities from classic linear regression that account for the combined model- and algorithm-induced uncertainties. (via Semantic Scholar)
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16. Peace, Justice and Strong Institutions (OpenAlex)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: November 17, 2021

2021 article

Multiplicative perturbation bounds for multivariate multiple linear regression in Schatten p-norms

Chi, J. T., & Ipsen, I. C. F. (2021, April 6). Linear Algebra and Its Applications, Vol. 624, pp. 87–102.

By: J. Chi n & I. Ipsen n 

author keywords: Projector Multiplicative perturbations; Moore Penrose inverse; Schatten p-norms; Multivariate multiple linear; regression
topics (OpenAlex): Advanced Statistical Methods and Models; Sensory Analysis and Statistical Methods; Statistical and numerical algorithms; Morphological variations and asymmetry
TL;DR: This work extends recent MLR analyses to sketched MMLR in general Schatten $p-norms by interpreting the sketched problem as a multiplicative perturbation, and derives expressions for the exact and perturbed solutions in terms of projectors for easy geometric interpretation. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: May 1, 2021

2021 article

SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-Based Gene-Environment Interaction Tests in Biobank Data

Chi, J. T., Ipsen, I. C. F., Hsiao, T.-H., Lin, C.-H., Wang, L.-S., Lee, W.-P., … Tzeng, J.-Y. (2021, November 2). Frontiers in Genetics, Vol. 11.

By: J. Chi n, I. Ipsen n , T. Hsiao *, C. Lin *, L. Wang *, W. Lee *, T. Lu *, J. Tzeng n 

author keywords: gene-based GxE test for biobank data; GxE collapsing test for biobank data; GxE test for large-scale sequencing data; scalable GEI test; gene-environment variance component test; gene-environment kernel test; regional-based gene-environment test
topics (OpenAlex): Bioinformatics and Genomic Networks; Genetic Associations and Epidemiology; Genetic Mapping and Diversity in Plants and Animals
TL;DR: SEAGLE, a Scalable Exact AlGorithm for Large-scale set-based G×E tests, is presented to permit GxE VC tests for biobank-scale data to explore the interaction of gene and physical activity status on body mass index and demonstrates its utility by conducting genome-wide gene-basedG×E analysis on the Taiwan Biobank data. (via Semantic Scholar)
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14. Life Below Water (OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: November 29, 2021

2020 article

Blind Hyperspectral Unmixing Based on Graph Total Variation Regularization

Qin, J., Lee, H., Chi, J. T., Drumetz, L., Chanussot, J., Lou, Y., & Bertozzi, A. L. (2020, September 18). IEEE Transactions on Geoscience and Remote Sensing.

By: J. Qin *, H. Lee *, J. Chi n, L. Drumetz *, J. Chanussot *, Y. Lou *, A. Bertozzi *

author keywords: Alternating direction method of multipliers (ADMM); blind hyperspectral unmixing; graph Laplacian; graph total variation (gTV); Nystrom method
topics (OpenAlex): Remote-Sensing Image Classification; Advanced Image Fusion Techniques; Sparse and Compressive Sensing Techniques
TL;DR: A novel blind hyperspectral unmixing model based on the graph total variation (gTV) regularization is proposed, which can be solved efficiently by the alternating direction method of multipliers (ADMM), and the Nyström method is applied to approximate a fully connected graph by a small subset of sampled points. (via Semantic Scholar)
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13. Climate Action (Web of Science)
15. Life on Land (Web of Science)
Source: Web Of Science
Added: May 24, 2021

2015 article

k-POD: A Method fork-Means Clustering of Missing Data

Chi, J. T., Chi, E. C., & Baraniuk, R. G. (2015, September 11). The American Statistician, Vol. 70, pp. 91–99.

By: J. Chi*, E. Chi*  & R. Baraniuk

author keywords: Clustering; k-means; Imputation; Majorization-minimization; Missing data
topics (OpenAlex): Advanced Clustering Algorithms Research; Bayesian Methods and Mixture Models; Soil Geostatistics and Mapping
TL;DR: The k-POD method presents a simple extension of k-means clustering for missing data that works even when the missingness mechanism is unknown, when external information is unavailable, and when there is significant missingness in the data. (via Semantic Scholar)
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Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 6, 2018

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