Name
Abstract | ||
|---|---|---|
In this paper, we consider the problem of selecting representatives from a data set for arbitrary supervised/unsupervised learning tasks. We identify a subset $S$ of a data set $A$ such that 1) the size of $S$ is much smaller than $A$ and 2) $S$ efficiently describes the entire data set, in a way formalized via auto-regression. The set $S$, also known as the exemplars of the data set $A$, is constructed by solving a convex auto-regressive version of dictionary learning where the dictionary and measurements are given by the data matrix. We show that in order to generate $|S| = k$ exemplars, our algorithm, Frank-Wolfe Sparse Representation (FWSR), only requires $approx k$ iterations with a per-iteration cost that is quadratic in the size of $A$, an order of magnitude faster than state of the art methods. We test our algorithm against current methods on 4 different data sets and are able to outperform other exemplar finding methods in almost all scenarios. We also test our algorithm qualitatively by selecting exemplars from a corpus of Donald Trump and Hillary Clintonu0027s twitter posts. |
Year | Venue | DocType |
|---|---|---|
2018 | arXiv: Learning | Journal |
Volume | Citations | PageRank |
abs/1811.02702 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gary Cheng | 1 | 0 | 2.03 |
| Armin Askari | 2 | 1 | 1.75 |
| Laurent El Ghaoui | 3 | 2935 | 315.98 |
| Kannan Ramchandran | 4 | 9401 | 1029.57 |