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pro vyhledávání: '"Strnadova‐Neeley, Veronika"'
Autor:
Madhyastha, Meghana, Li, Percy, Browne, James, Strnadova-Neeley, Veronika, Priebe, Carey E., Burns, Randal, Vogelstein, Joshua T.
Geodesic distance is the shortest path between two points in a Riemannian manifold. Manifold learning algorithms, such as Isomap, seek to learn a manifold that preserves geodesic distances. However, such methods operate on the ambient dimensionality,
Externí odkaz:
http://arxiv.org/abs/1907.02844
Akademický článek
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Recommender system data presents unique challenges to the data mining, machine learning, and algorithms communities. The high missing data rate, in combination with the large scale and high dimensionality that is typical of recommender systems data,
Externí odkaz:
http://arxiv.org/abs/1608.08646
Autor:
Strnadova-Neeley, Veronika
Publikováno v:
Strnadova-Neeley, Veronika. (2018). Exploiting Intrinsic Clustering Structure in Discrete-Valued Data Sets for Efficient Knowledge Discovery in the Presence of Missing Data. 0035: Computer Science. Retrieved from: http://www.escholarship.org/uc/item/92m7r8j9
Scalable algorithm design has become central in the era of large-scale data analysis. The vast amounts of data pouring in from a diverse set of application domains, such as bioinformatics, recommender systems, sensor systems, and social networks, can
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______325::acd90da86a806b04d40e47b3df0e492f
http://www.escholarship.org/uc/item/92m7r8j9
http://www.escholarship.org/uc/item/92m7r8j9