Zobrazeno 1 - 10
of 90
pro vyhledávání: '"Moon, Kevin R."'
The value of supervised dimensionality reduction lies in its ability to uncover meaningful connections between data features and labels. Common dimensionality reduction methods embed a set of fixed, latent points, but are not capable of generalizing
Externí odkaz:
http://arxiv.org/abs/2406.04421
Data visualization via dimensionality reduction is an important tool in exploratory data analysis. However, when the data are noisy, many existing methods fail to capture the underlying structure of the data. The method called Empirical Intrinsic Geo
Externí odkaz:
http://arxiv.org/abs/2406.03396
Symmetry detection has been shown to improve various machine learning tasks. In the context of continuous symmetry detection, current state of the art experiments are limited to the detection of affine transformations. Under the manifold assumption,
Externí odkaz:
http://arxiv.org/abs/2406.03619
Deep learning identification models have shown promise for identifying gas plumes in Longwave IR hyperspectral images of urban scenes, particularly when a large library of gases are being considered. Because many gases have similar spectral signature
Externí odkaz:
http://arxiv.org/abs/2401.13068
Multi-domain data is becoming increasingly common and presents both challenges and opportunities in the data science community. The integration of distinct data-views can be used for exploratory data analysis, and benefit downstream analysis includin
Externí odkaz:
http://arxiv.org/abs/2210.12774
The integration of multimodal data presents a challenge in cases when the study of a given phenomena by different instruments or conditions generates distinct but related domains. Many existing data integration methods assume a known one-to-one corre
Externí odkaz:
http://arxiv.org/abs/2206.07305
Random forests are considered one of the best out-of-the-box classification and regression algorithms due to their high level of predictive performance with relatively little tuning. Pairwise proximities can be computed from a trained random forest a
Externí odkaz:
http://arxiv.org/abs/2201.12682
Unmanned aerial vehicles (UAV) often rely on GPS for navigation. GPS signals, however, are very low in power and easily jammed or otherwise disrupted. This paper presents a method for determining the navigation errors present at the beginning of a GP
Externí odkaz:
http://arxiv.org/abs/2010.12108
Publikováno v:
IEEE International Conference on Big Data, pp. 5027-5036, Dec. 2020
A fundamental task in data exploration is to extract simplified low dimensional representations that capture intrinsic geometry in data, especially for faithfully visualizing data in two or three dimensions. Common approaches to this task use kernel
Externí odkaz:
http://arxiv.org/abs/2007.07142
Dimensionality reduction is often used as an initial step in data exploration, either as preprocessing for classification or regression or for visualization. Most dimensionality reduction techniques to date are unsupervised; they do not take class la
Externí odkaz:
http://arxiv.org/abs/2006.08701