Maximum Relevance Minimum Redundancy Dropout with Informative Kernel Determinantal Point Process
نویسندگان
چکیده
منابع مشابه
Diverse Personalization with Determinantal Point Process Eigenmixtures
Personalization has become an important part of recommendation systems for online products including news, search, media and advertising. Real world recommender systems need to also take into account the diversity and serendipity of the set of recommended items so as to not overwhelm the user with too similar items and to discover user interests that were previously unknown to the system (Szpek...
متن کاملMaximum likelihood estimation of determinantal point processes
Determinantal point processes (DPPs) have wide-ranging applications in machine learning, where they are used to enforce the notion of diversity in subset selection problems. Many estimators have been proposed, but surprisingly the basic properties of the maximum likelihood estimator (MLE) have received little attention. The difficulty is that it is a non-concave maximization problem, and such f...
متن کاملDeterminantal point process models on the sphere
We consider determinantal point processes on the d-dimensional unit sphere Sd. These are finite point processes exhibiting repulsiveness and with moment properties determined by a certain determinant whose entries are specified by a so-called kernel which we assume is a complex covariance function defined on Sd× Sd. We review the appealing properties of such processes, including their specific ...
متن کاملDeterminantal point process models and statistical inference
Statistical models and methods for determinantal point processes (DPPs) seem largely unexplored. We demonstrate that DPPs provide useful models for the description of repulsive spatial point processes, particularly in the ‘soft-core’ case. Such data are usually modelled by Gibbs point processes, where the likelihood and moment expressions are intractable and simulations are time consuming. We e...
متن کاملActive Learning with Maximum Density and Minimum Redundancy
Active Learning is a machine learning technique that selects the most informative examples for labeling so that the classification performance would be improved to its maximum possibility. In this paper, a novel active learning approach based on Maximum Density and Minimum Redundancy (MDMR) is proposed. The objective of MDMR is to select a set of examples that have large density and small redun...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Sensors
سال: 2021
ISSN: 1424-8220
DOI: 10.3390/s21051846