Dictionary learning is a prototypical machine learning problem with broad applications throughout computer vision and signal processing. Given a set of training data, the goal is to learn a dictionary of prototype patterns that provide a sparse (parsimonious) representation of the data. Such a dictionary then enables a variety of problems to be solved by exploiting sparse structure; example problems include noise removal, super-resolution, deblurring, imputing missing data, and classification. Like many problems in machine learning, dictionary learning can be formulated as an optimization problem. Unfortunately, this optimization problem is non-convex, making it potentially difficult to solve to global optimality. Additional questions arise in how to deal with multi-modal data and distributed data, such as might be collected by multiple satellites, UAVs, etc. This presentation will summarize our team’s research on theory and algorithms for solving non-convex matrix-valued optimization problems such as dictionary learning, particularly in multi-modal, distributed, and streaming contexts. We welcome the opportunity to discuss further transitioning this technology for Naval applications.

Fast Communication-efficient Algorithms for Distributed and Multi-modal Dictionary Learning

**Moderator:** [Dr. Nathan VanHoudnos](https://www.underline.io/speakers/77633-nathan-vanhoudnos)

**Speakers:**

[Jack Dolan](https://www.underline.io/speakers/77550-jack-dolan)

[Christopher Griffin](https://www.underline.io/speakers/77540-christopher-griffin)

Colin Olson

[Michael Wakin](https://www.underline.io/speakers/77570-michael-wakin)

Learning Methods

technical paper

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

**Watch this video to hear about the AFCEA purpose.**

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Michael Wakin

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Fast Communication-efficient Algorithms for Distributed and Multi-modal Dictionary Learning

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