Search Results for author: Md. Ashad Alam

Found 9 papers, 0 papers with code

A generalized kernel machine approach to identify higher-order composite effects in multi-view datasets

no code implementations29 Apr 2020 Md. Ashad Alam, Chuan Qiu, Hui Shen, Yu-Ping Wang, Hong-Wen Deng

In this paper, we propose a novel generalized kernel machine approach to identify higher-order composite effects in multi-view biomedical datasets.

Ecological Data Analysis Based on Machine Learning Algorithms

no code implementations21 Dec 2018 Md. Siraj-Ud-Doula, Md. Ashad Alam

In our study we conclude that Linear Discriminant Analysis and k-nearest neighbors are the best methods among all other methods

Classification General Classification

Gene Shaving using influence function of a kernel method

no code implementations5 Sep 2018 Md. Ashad Alam, Mohammad Shahjama, Md. Ferdush Rahman

Identifying significant subsets of the genes, gene shaving is an essential and challenging issue for biomedical research for a huge number of genes and the complex nature of biological networks,.

Kernel Method for Detecting Higher Order Interactions in multi-view Data: An Application to Imaging, Genetics, and Epigenetics

no code implementations14 Jul 2017 Md. Ashad Alam, Hui-Yi Lin, Vince Calhoun, Yu-Ping Wang

In this study, we tested the interaction effect of multimodal datasets using a novel method called the kernel method for detecting higher order interactions among biologically relevant mulit-view data.

Influence Function and Robust Variant of Kernel Canonical Correlation Analysis

no code implementations9 May 2017 Md. Ashad Alam, Kenji Fukumizu, Yu-Ping Wang

Many unsupervised kernel methods rely on the estimation of the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO).

Learning Schizophrenia Imaging Genetics Data Via Multiple Kernel Canonical Correlation Analysis

no code implementations15 Sep 2016 Owen Richfield, Md. Ashad Alam, Vince Calhoun, Yu-Ping Wang

Kernel and Multiple Kernel Canonical Correlation Analysis (CCA) are employed to classify schizophrenic and healthy patients based on their SNPs, DNA Methylation and fMRI data.

Classification General Classification

Identifying Outliers using Influence Function of Multiple Kernel Canonical Correlation Analysis

no code implementations1 Jun 2016 Md. Ashad Alam, Yu-Ping Wang

Second, we propose an IF of multiple kernel CCA, which can be applied for more than two datasets.

Gene-Gene association for Imaging Genetics Data using Robust Kernel Canonical Correlation Analysis

no code implementations1 Jun 2016 Md. Ashad Alam, Osamu Komori, Yu-Ping Wang

Third, we propose a nonparametric robust KCCU method based on robust kernel CCA, which is designed for contaminated data and less sensitive to noise than classical kernel CCA.

Robust Kernel (Cross-) Covariance Operators in Reproducing Kernel Hilbert Space toward Kernel Methods

no code implementations17 Feb 2016 Md. Ashad Alam, Kenji Fukumizu, Yu-Ping Wang

Finally, we propose a method based on robust kernel CO and robust kernel CCO, called robust kernel CCA, which is designed for contaminated data and less sensitive to noise than classical kernel CCA.

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