We investigate an application of probabilistic latent semantics to the problem of device usage analysis in an infrastructure in which multiple users have access to a shared pool of devices capable of delivering different kinds of service and service levels. Each invocation of a service by a user, called a job, is assumed to be logged simply as a co-occurence of the identifier of the user and that of the device used. The data is best modeled by assuming that multiple latent variables (instead of a single one as in traditional PLSA) satisfying different types of constraints explain the observed variables of a job. We discuss the application of our model to a printing infrastructure in an office environment. Here, a job is assumed to be generated by two independent factors: the location of the user and the service class of the job (eg. black and white vs. colour, high volume vs. low volume etc.).
Probabilistic Latent Clustering of Device Usage
| Jean-Marc Andreoli, Guillaume Bouchard |
| IDA 2005, the 6th International Symposium on Intelligent Data Analysis, Madrid, Spain, September 8-10, 2005. |
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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