27 citations · 63 across the 6 of their papers we have counts for
8 papers
From Two-Class Linear Discriminant Analysis to Interpretable Multilayer Perceptron Design
Ruiyuan Lin, Zhiruo Zhou, Suya You +2
A closed-form solution exists in two-class linear discriminant analysis (LDA), which discriminates two Gaussian-distributed classes in a multi-dimensional feature space. In this wo…
Object Detection on Single Monocular Images through Canonical Correlation Analysis
Zifan Yu, Suya You
Without using extra 3-D data like points cloud or depth images for providing 3-D information, we retrieve the 3-D object information from single monocular images. The high-quality…
PixelHop++: A Small Successive-Subspace-Learning-Based (SSL-based) Model for Image Classification
Yueru Chen, Mozhdeh Rouhsedaghat, Suya You +2
The successive subspace learning (SSL) principle was developed and used to design an interpretable learning model, known as the PixelHop method,for image classification in our prio…
Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion
Yiqi Zhong, Cho-Ying Wu, Suya You +1
In this paper, we propose our Correlation For Completion Network (CFCNet), an end-to-end deep learning model that uses the correlation between two data sources to perform sparse de…
Visualization, Discriminability and Applications of Interpretable Saak Features
Abinaya Manimaran, Thiyagarajan Ramanathan, Suya You +1
In this work, we study the power of Saak features as an effort towards interpretable deep learning. Being inspired by the operations of convolutional layers of convolutional neural…
Robustness Of Saak Transform Against Adversarial Attacks
Thiyagarajan Ramanathan, Abinaya Manimaran, Suya You +1
Image classification is vulnerable to adversarial attacks. This work investigates the robustness of Saak transform against adversarial attacks towards high performance image classi…