Results 81 to 90 of about 1,253,621 (308)

A blocking and regularization approach to high dimensional realized covariance estimation [PDF]

, 2009
We introduce a regularization and blocking estimator for well-conditioned high-dimensional daily covariances using high-frequency data. Using the Barndorff-Nielsen, Hansen, Lunde, and Shephard (2008a) kernel estimator, we estimate the covariance matrix ...
Hautsch, Nikolaus, Lada M. Kyj, Oomen, Roel C.A., Roel C. A. Oomen, Nikolaus Hautsch, Kyj, Lada M., Oomen, Roel C. A.   +6 more
core   +1 more source

K-means clustering based filter feature selection on high dimensional data

IJAIN (International Journal of Advances in Intelligent Informatics), 2016
With hundreds or thousands of features in high dimensional data, computational workload is challenging. In classification process, features which do not contribute significantly to prediction of classes, add to the computational workload.
Dewi Pramudi Ismi, Shireen Panchoo, Murinto Murinto   +2 more
doaj   +1 more source

Low-dimensional embeddings of high-dimensional data

CoRR
Large collections of high-dimensional data have become nearly ubiquitous across many academic fields and application domains, ranging from biology to the humanities. Since working directly with high-dimensional data poses challenges, the demand for algorithms that create low-dimensional representations, or embeddings, for data visualization ...
Cyril de Bodt, Alex Diaz-Papkovich, Michael Bleher, Kerstin Bunte, Corinna Coupette, Sebastian Damrich, Enrique Fita Sanmartin, Fred A. Hamprecht, Emoke-Ágnes Horvát, Dhruv Kohli, Smita Krishnaswamy, John A. Lee 0001, Boudewijn P. F. Lelieveldt, Leland McInnes, Ian T. Nabney, Maximilian Noichl, Pavlin G. Policar, Bastian Rieck, Guy Wolf, Gal Mishne, Dmitry Kobak   +20 more
openaire   +4 more sources

Fitting High-Dimensional Copulae to Data [PDF]

, 2011
This paper make an overview of the copula theory from a practical side. We consider different methods of copula estimation and different Goodness-of-Fit tests for model selection. In the GoF section we apply Kolmogorov-Smirnov and Cramer-von-Mises type tests and calculate power of these tests under different assumptions.
openaire   +4 more sources

High-dimensional data

Journal of the National Science Foundation of Sri Lanka, 2016
This paper provides a brief introduction to high-dimensional data, a form of ‘Big Data’, and gives an overview of several data analysis concepts and techniques that could be used to explore and analyse such data. An example that involves genomics data from several Sri Lankan and United Kingdom oral cancer patients is used to illustrate the methods.
Dhammika Amaratunga, Javier Cabrera
openaire   +2 more sources

Learning to visualise high-dimensional data [PDF]

Proceedings. Eighth International Conference on Information Visualisation, 2004. IV 2004., 2004
Visualisation techniques focus on reducing high dimensional data to a low dimensional surface or a cube. Similar dimensional reduction is attempted in the so-called 'self-organising maps'. A number of techniques have been developed to visualise categories learnt by these maps through and exemplified by the term sequential clustering.
Ahmad, Khurshid, Vrusias, Bogdan
openaire   +2 more sources

Interpreting the effects of DNA polymerase variants at the structural level

Molecular Oncology, EarlyView.
Using MAVISp and molecular dynamics simulations, we analyzed over 60 000 missense variants in POLE and POLD1 from ClinVar, COSMIC, cBioPortal, and saturation mutagenesis. Identified mechanistic indicators, including stability, binding, and long‐range, enable structural interpretation, providing ACMG‐like evidence for possible reclassification of VUS ...
Matteo Arnaudi, Karolina Krzesińska, Ludovica Beltrame, Pablo Sánchez‐Izquierdo Besora, Matteo Tiberti, Mef Nilbert, Anna Rohlin, Elena Papaleo   +7 more
wiley   +1 more source

Detecting anomalies from high-dimensional wireless network data streams: a case study

, 2011
In this paper, we study the problem of anomaly detection in wireless network streams. We have developed a new technique, called Stream Projected Outlier deTector (SPOT), to deal with the problem of anomaly detection from multi-dimensional or high ...
Zhang, Ji, Wang, Hai, Gao, Qigang, Wang, Hua   +3 more
core   +1 more source

Proteasome inhibitor, ixazomib prevents topoisomerase‐I degradation and reverses irinotecan resistance in colorectal cancer

Molecular Oncology, EarlyView.
Ixazomib inhibits proteasome‐mediated degradation of topoisomerase I induced by irinotecan, thereby restoring drug sensitivity and promoting tumor cell death in colorectal cancer. Irinotecan, a topoisomerase I (topoI) inhibitor, is widely used for colorectal cancer, but resistance remains a major clinical challenge.
Yuho Ebata, Koji Ando, Hirofumi Hasuda, Koshi Mimori, Elizabeth C. Unan, Siddhartha Pulukuri, Aahana Tiku, Allison Berger, Eiji Oki, Ajit Bharti, Tomoharu Yoshizumi   +10 more
wiley   +1 more source

High-dimensional Structured Additive Regression Models: Bayesian Regularisation, Smoothing and Predictive Performance [PDF]

, 2009
Data structures in modern applications frequently combine the necessity of flexible regression techniques such as nonlinear and spatial effects with high-dimensional covariate vectors. While estimation of the former is typically achieved by supplementing
Konrath, Susanne, Kneib, Thomas, Fahrmeir, Ludwig   +2 more
core   +1 more source