Results 271 to 280 of about 446,074 (292)

Column and row subset selection using nuclear scores: algorithms and theory for Nyström approximation, CUR decomposition, and graph Laplacian reduction

arXiv.org
Column selection is an essential tool for structure-preserving low-rank approximation, with wide-ranging applications across many fields, such as data science, machine learning, and theoretical chemistry.
M. Fornace, Michael Lindsey
semanticscholar   +1 more source

Applying Genetic Algorithms to Validate a Conjecture in Graph Theory: The Minimum Dominating Set Problem

Mexican International Conference on Artificial Intelligence, 2023
J. Cervantes-Ojeda, María del Carmen Gómez Fuentes, Julián Fresán-Figueroa   +2 more
semanticscholar   +1 more source

A novel graph-based multiple kernel learning framework for hyperspectral image classification

International Journal of Remote Sensing
Multiple kernel learning (MKL) is an efficient way to improve hyperspectral image classification with few training samples by integrating spectral and spatial features.
Shirin Hassanzadeh, H. Danyali, A. Karami, M. Helfroush   +3 more
semanticscholar   +1 more source

BANGS: Game-Theoretic Node Selection for Graph Self-Training

International Conference on Learning Representations
Graph self-training is a semi-supervised learning method that iteratively selects a set of unlabeled data to retrain the underlying graph neural network (GNN) model and improve its prediction performance. While selecting highly confident nodes has proven
Fangxin Wang, Kay Liu, Sourav Medya, Philip S. Yu   +3 more
semanticscholar   +1 more source

Understanding Deep Learning via Notions of Rank

arXiv.org
Despite the extreme popularity of deep learning in science and industry, its formal understanding is limited. This thesis puts forth notions of rank as key for developing a theory of deep learning, focusing on the fundamental aspects of generalization ...
Noam Razin
semanticscholar   +1 more source

The Rank-Ramsey Problem and the Log-Rank Conjecture

arXiv.org
A graph is called Rank-Ramsey if (i) Its clique number is small, and (ii) The adjacency matrix of its complement has small rank. We initiate a systematic study of such graphs.
Gal Beniamini, N. Linial, Adi Shraibman
semanticscholar   +1 more source

Generalized Graph Signal Sampling under Subspace Priors by Difference-of-Convex Minimization

Asia-Pacific Signal and Information Processing Association Annual Summit and Conference
This paper proposes an effective approach for sampling graph signals under the subspace prior. Unlike conventional methods that assume bandlimited signals, our method, based on generalized sampling theory, designs a sampling operator suitable for general
Keitaro Yamashita, Kazuki Naganuma, Shunsuke Ono   +2 more
semanticscholar   +1 more source

Algebraic Graph Theory

Graduate texts in mathematics, 2001
Christopher D. Godsil, G. Royle
semanticscholar   +1 more source

Graph Theory

, 2016
Frank de Zeeuw
semanticscholar   +1 more source

Dimensional reduction of 3D building models using graph theory and its application in building energy simulation

Engineering computations, 2007
C. Treeck, E. Rank
semanticscholar   +1 more source