Matrix factorization techniques in machine learning, signal processing, and statistics
Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …
compressible signals. Sparse coding represents a signal as a sparse linear combination of …
Inter-class sparsity based discriminative least square regression
Least square regression is a very popular supervised classification method. However, two
main issues greatly limit its performance. The first one is that it only focuses on fitting the …
main issues greatly limit its performance. The first one is that it only focuses on fitting the …
Non-negative spectral learning and sparse regression-based dual-graph regularized feature selection
Feature selection is an important approach for reducing the dimension of high-dimensional
data. In recent years, many feature selection algorithms have been proposed, but most of …
data. In recent years, many feature selection algorithms have been proposed, but most of …
Discriminative autoencoder for feature extraction: Application to character recognition
A Gogna, A Majumdar - Neural Processing Letters, 2019 - Springer
Conventionally, autoencoders are unsupervised representation learning tools. In this work,
we propose a novel discriminative autoencoder. Use of supervised discriminative learning …
we propose a novel discriminative autoencoder. Use of supervised discriminative learning …
Matrix factorization-based data fusion for the prediction of RNA-binding proteins and alternative splicing event associations during epithelial–mesenchymal transition
Motivation The epithelial-mesenchymal transition (EMT) is a cellular–developmental
process activated during tumor metastasis. Transcriptional regulatory networks controlling …
process activated during tumor metastasis. Transcriptional regulatory networks controlling …
Orthogonal self-guided similarity preserving projection for classification and clustering
A suitable feature representation can faithfully preserve the intrinsic structure of data.
However, traditional dimensionality reduction (DR) methods commonly use the original input …
However, traditional dimensionality reduction (DR) methods commonly use the original input …
Signature verification using convolutional neural network
Signatures are widely used to validate the authentication of an individual. A robust method is
still awaited that can correctly certify the authenticity of a signature. The proposed solution …
still awaited that can correctly certify the authenticity of a signature. The proposed solution …
Toward data quality analytics in signature verification using a convolutional neural network
Many studies have been conducted on Handwritten Signature Verification. Researchers
have taken many different approaches to accurately identify valid signatures from skilled …
have taken many different approaches to accurately identify valid signatures from skilled …
Elastic nonnegative matrix factorization
H Xiong, D Kong - Pattern Recognition, 2019 - Elsevier
Nonnegative matrix factorization (NMF) plays a vital role in data mining and machine
learning fields. Standard NMF utilizes the Frobenius norm while robust NMF uses the robust …
learning fields. Standard NMF utilizes the Frobenius norm while robust NMF uses the robust …
Multi-Similarities Bilinear Matrix Factorization-Based Method for Predicting Human Microbe–Disease Associations
X Yang, L Kuang, Z Chen, L Wang - Frontiers in Genetics, 2021 - frontiersin.org
Accumulating studies have shown that microbes are closely related to human diseases. In
this paper, a novel method called MSBMFHMDA was designed to predict potential microbe …
this paper, a novel method called MSBMFHMDA was designed to predict potential microbe …