scholarly journals Subsampling and Aggregation: A Solution to the Scalability Problem in Distance-Based Prediction for Mixed-Type Data

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2247
Author(s):  
Amparo Baíllo ◽  
Aurea Grané
Keyword(s):  
Mixed Type ◽  
Computational Cost ◽  
Distance Matrix ◽  
Classification Problem ◽  
Gram Matrix ◽  
Aggregation Techniques ◽  
Regression Techniques ◽  
Type Data ◽  
Classical Linear Regression ◽  
Available Information

The distance-based linear model (DB-LM) extends the classical linear regression to the framework of mixed-type predictors or when the only available information is a distance matrix between regressors (as it sometimes happens with big data). The main drawback of these DB methods is their computational cost, particularly due to the eigendecomposition of the Gram matrix. In this context, ensemble regression techniques provide a useful alternative to fitting the model to the whole sample. This work analyzes the performance of three subsampling and aggregation techniques in DB regression on two specific large, real datasets. We also analyze, via simulations, the performance of bagging and DB logistic regression in the classification problem with mixed-type features and large sample sizes.

scholarly journals Retinal vessel segmentation with constrained-based nonnegative matrix factorization and 3D modified attention U-Net

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yang Yu ◽  
Hongqing Zhu
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Computational Cost ◽  
Three Dimensional ◽  
Nonnegative Matrix ◽  
Retinal Vessel ◽  
Classification Problem ◽  
Image Resolution ◽  
Retinal Vessels ◽  
Segmentation Task

AbstractDue to the complex morphology and characteristic of retinal vessels, it remains challenging for most of the existing algorithms to accurately detect them. This paper proposes a supervised retinal vessels extraction scheme using constrained-based nonnegative matrix factorization (NMF) and three dimensional (3D) modified attention U-Net architecture. The proposed method detects the retinal vessels by three major steps. First, we perform Gaussian filter and gamma correction on the green channel of retinal images to suppress background noise and adjust the contrast of images. Then, the study develops a new within-class and between-class constrained NMF algorithm to extract neighborhood feature information of every pixel and reduce feature data dimension. By using these constraints, the method can effectively gather similar features within-class and discriminate features between-class to improve feature description ability for each pixel. Next, this study formulates segmentation task as a classification problem and solves it with a more contributing 3D modified attention U-Net as a two-label classifier for reducing computational cost. This proposed network contains an upsampling to raise image resolution before encoding and revert image to its original size with a downsampling after three max-pooling layers. Besides, the attention gate (AG) set in these layers contributes to more accurate segmentation by maintaining details while suppressing noises. Finally, the experimental results on three publicly available datasets DRIVE, STARE, and HRF demonstrate better performance than most existing methods.

Integrated dimensionality reduction technique for mixed-type data involving categorical values

2016 ◽  
Vol 43 ◽  
pp. 199-209 ◽  
Author(s):  
Chung-Chian Hsu ◽  
Wei-Hao Huang
Keyword(s):  
Dimensionality Reduction ◽  
Mixed Type ◽  
Reduction Technique ◽  
Dimensionality Reduction Technique ◽  
Type Data

Growing Self-Organizing Map with cross insert for mixed-type data clustering

2012 ◽  
Vol 12 (9) ◽  
pp. 2856-2866 ◽  
Author(s):  
Wei-Shen Tai ◽  
Chung-Chian Hsu
Keyword(s):  
Data Clustering ◽  
Mixed Type ◽  
Self Organizing Map ◽  
Type Data ◽  
Self Organizing

DeepCPP: a deep neural network based on nucleotide bias information and minimum distribution similarity feature selection for RNA coding potential prediction

2020 ◽  
Author(s):  
Yu Zhang ◽  
Cangzhi Jia ◽  
Melissa Jane Fullwood ◽  
Chee Keong Kwoh
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Deep Neural Network ◽  
Feature Selection Method ◽  
Classification Problem ◽  
Open Reading Frames ◽  
Nucleotide Bias ◽  
Sequencing Technologies ◽  
Type Data ◽  
Coding Potential

Abstract The development of deep sequencing technologies has led to the discovery of novel transcripts. Many in silico methods have been developed to assess the coding potential of these transcripts to further investigate their functions. Existing methods perform well on distinguishing majority long noncoding RNAs (lncRNAs) and coding RNAs (mRNAs) but poorly on RNAs with small open reading frames (sORFs). Here, we present DeepCPP (deep neural network for coding potential prediction), a deep learning method for RNA coding potential prediction. Extensive evaluations on four previous datasets and six new datasets constructed in different species show that DeepCPP outperforms other state-of-the-art methods, especially on sORF type data, which overcomes the bottleneck of sORF mRNA identification by improving more than 4.31, 37.24 and 5.89% on its accuracy for newly discovered human, vertebrate and insect data, respectively. Additionally, we also revealed that discontinuous k-mer, and our newly proposed nucleotide bias and minimal distribution similarity feature selection method play crucial roles in this classification problem. Taken together, DeepCPP is an effective method for RNA coding potential prediction.

Co-clustering Based Exploratory Analysis of Mixed-Type Data Tables

2019 ◽  
pp. 23-41 ◽  
Author(s):  
Aichetou Bouchareb ◽  
Marc Boullé ◽  
Fabrice Clérot ◽  
Fabrice Rossi
Keyword(s):  
Mixed Type ◽  
Exploratory Analysis ◽  
Data Tables ◽  
Type Data

Cluster Analysis: An Application to a Real Mixed-Type Data Set

2018 ◽  
pp. 525-533 ◽  
Author(s):  
G. Caruso ◽  
S. A. Gattone ◽  
A. Balzanella ◽  
T. Di Battista
Keyword(s):  
Cluster Analysis ◽  
Mixed Type ◽  
Data Set ◽  
Type Data

scholarly journals Clustering of Mixed-Type Data Considering Concept Hierarchies

2019 ◽  
pp. 555-573 ◽  
Author(s):  
Sahar Behzadi ◽  
Nikola S. Müller ◽  
Claudia Plant ◽  
Christian Böhm
Keyword(s):  
Mixed Type ◽  
Type Data ◽  
Concept Hierarchies
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