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no code implementations • 30 Jun 2020 • Jay S. Stanley III, Eric C. Chi, Gal Mishne

Graph signal processing (GSP) is an important methodology for studying data residing on irregular structures.

1 code implementation • 24 Apr 2019 • Halley L. Brantley, Joseph Guinness, Eric C. Chi

Through simulation studies and our motivating application to low cost air quality sensor data, we demonstrate that our model provides better quantile trend estimates than existing methods and improves signal classification of low-cost air quality sensor output.

Methodology Applications Computation

no code implementations • 16 Oct 2018 • Gal Mishne, Eric C. Chi, Ronald R. Coifman

We propose utilizing this coupled structure to perform co-manifold learning: uncovering the underlying geometry of both the rows and the columns of a given matrix, where we focus on a missing data setting.

no code implementations • 28 Jun 2018 • Eric C. Chi, Stefan Steinerberger

Convex clustering refers, for given $\left\{x_1, \dots, x_n\right\} \subset \mathbb{R}^p$, to the minimization of \begin{eqnarray*} u(\gamma) & = & \underset{u_1, \dots, u_n }{\arg\min}\;\sum_{i=1}^{n}{\lVert x_i - u_i \rVert^2} + \gamma \sum_{i, j=1}^{n}{w_{ij} \lVert u_i - u_j\rVert},\\ \end{eqnarray*} where $w_{ij} \geq 0$ is an affinity that quantifies the similarity between $x_i$ and $x_j$.

no code implementations • 17 Mar 2018 • Eric C. Chi, Brian R. Gaines, Will Wei Sun, Hua Zhou, Jian Yang

Our convex co-clustering (CoCo) estimator enjoys stability guarantees and its computational and storage costs are polynomial in the size of the data.

no code implementations • NeurIPS 2017 • Jason Xu, Eric C. Chi, Kenneth Lange

Estimation in generalized linear models (GLM) is complicated by the presence of constraints.

no code implementations • 16 Dec 2016 • Jason Xu, Eric C. Chi, Meng Yang, Kenneth Lange

Furthermore, we show that the Euclidean norm appearing in the proximity function of the non-linear split feasibility problem can be replaced by arbitrary Bregman divergences.

1 code implementation • 16 Aug 2016 • Bethany Lusch, Eric C. Chi, J. Nathan Kutz

We consider $N$-way data arrays and low-rank tensor factorizations where the time mode is coded as a sparse linear combination of temporal elements from an over-complete library.

no code implementations • 5 Aug 2014 • Eric C. Chi, Genevera I. Allen, Richard G. Baraniuk

In the biclustering problem, we seek to simultaneously group observations and features.

no code implementations • 1 Apr 2013 • Eric C. Chi, Kenneth Lange

In contrast to previously considered algorithms, our ADMM and AMA formulations provide simple and unified frameworks for solving the convex clustering problem under the previously studied norms and open the door to potentially novel norms.

no code implementations • 16 Nov 2012 • Eric C. Chi, Hua Zhou, Kenneth Lange

The problem of minimizing a continuously differentiable convex function over an intersection of closed convex sets is ubiquitous in applied mathematics.

no code implementations • 11 Dec 2011 • Eric C. Chi, Tamara G. Kolda

We present a new algorithm for Poisson tensor factorization called CANDECOMP-PARAFAC Alternating Poisson Regression (CP-APR) that is based on a majorization-minimization approach.

Numerical Analysis

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