Cover Image

Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yang, Yang, Sun, Hongjian, Gong, Jialei, Yu, Di
Format:	Preprint
Published:	2022
Subjects:	Computer Vision and Pattern Recognition Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

The Deepfake Detective: Interpreting Neural Forensics Through Sparse Features and Manifolds
by: Sahoo, Subramanyam, et al.
Published: (2025)

Measuring Feature Dependency of Neural Networks by Collapsing Feature Dimensions in the Data Manifold
by: Jin, Yinzhu, et al.
Published: (2024)

GNSP: Gradient Null Space Projection for Preserving Cross-Modal Alignment in VLMs Continual Learning
by: Peng, Tiantian, et al.
Published: (2025)

Preserving Silent Features for Domain Generalization
by: Zhao, Chujie, et al.
Published: (2024)

A Geometric Unification of Generative AI with Manifold-Probabilistic Projection Models
by: Bar, Leah, et al.
Published: (2025)

ManifoldGD: Training-Free Hierarchical Manifold Guidance for Diffusion-Based Dataset Distillation
by: Roy, Ayush, et al.
Published: (2026)

Bridging Domains with Approximately Shared Features
by: Zhong, Ziliang Samuel, et al.
Published: (2024)

Conceptual Learning via Embedding Approximations for Reinforcing Interpretability and Transparency
by: Dikter, Maor, et al.
Published: (2024)

CubistMerge: Spatial-Preserving Token Merging For Diverse ViT Backbones
by: Gong, Wenyi, et al.
Published: (2025)

Two-Stage Hierarchical and Explainable Feature Selection Framework for Dimensionality Reduction in Sleep Staging
by: Deng, Yangfan, et al.
Published: (2024)

Feature Map Similarity Reduction in Convolutional Neural Networks
by: Belmekki, Zakariae, et al.
Published: (2024)

Sparse Modelling for Feature Learning in High Dimensional Data
by: Neelam, Harish, et al.
Published: (2024)

Compact Vision Transformer by Reduction of Kernel Complexity
by: Wang, Yancheng, et al.
Published: (2025)

What's Inside Your Diffusion Model? A Score-Based Riemannian Metric to Explore the Data Manifold
by: Azeglio, Simone, et al.
Published: (2025)

Perturbation on Feature Coalition: Towards Interpretable Deep Neural Networks
by: Hu, Xuran, et al.
Published: (2024)

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features
by: Helbling, Alec, et al.
Published: (2025)

Self-Supervised Discriminative Feature Learning for Deep Multi-View Clustering
by: Xu, Jie, et al.
Published: (2021)

Concept-Centric Token Interpretation for Vector-Quantized Generative Models
by: Yang, Tianze, et al.
Published: (2025)

CORP: Closed-Form One-shot Representation-Preserving Structured Pruning for Transformers
by: Zhang, Boxiang, et al.
Published: (2026)

Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability
by: Bhalla, Usha, et al.
Published: (2023)

Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image Segmentation
by: Woodland, McKell, et al.
Published: (2024)

Spatio-temporal Prompting Network for Robust Video Feature Extraction
by: Sun, Guanxiong, et al.
Published: (2024)

Dissecting and Mitigating Diffusion Bias via Mechanistic Interpretability
by: Shi, Yingdong, et al.
Published: (2025)

Spectral Roll-off Points Variations: Exploring Useful Information in Feature Maps by Its Variations
by: Yu, Yunkai, et al.
Published: (2021)

Structure Preserving Diffusion Models
by: Lu, Haoye, et al.
Published: (2024)

TensorProjection Layer: A Tensor-Based Dimension Reduction Method in Deep Neural Networks
by: Morimoto, Toshinari, et al.
Published: (2020)

From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection
by: Vandenhirtz, Moritz, et al.
Published: (2025)

CIP-Net: Continual Interpretable Prototype-based Network
by: Di Valerio, Federico, et al.
Published: (2025)

ProFeAT: Projected Feature Adversarial Training for Self-Supervised Learning of Robust Representations
by: Addepalli, Sravanti, et al.
Published: (2024)

Manifold Learning for Hyperspectral Images
by: Harkat, Fethi, et al.
Published: (2025)

Flows and Diffusions on the Neural Manifold
by: Saragih, Daniel, et al.
Published: (2025)

Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient
by: Wu, Yongliang, et al.
Published: (2024)

Non-Negative Stiefel Approximating Flow: Orthogonalish Matrix Optimization for Interpretable Embeddings
by: Avants, Brian B., et al.
Published: (2025)

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selections and Approximations
by: Fang, Kun, et al.
Published: (2025)

Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons
by: Martel, E., et al.
Published: (2024)

CLIP-PAE: Projection-Augmentation Embedding to Extract Relevant Features for a Disentangled, Interpretable, and Controllable Text-Guided Face Manipulation
by: Zhou, Chenliang, et al.
Published: (2022)

The Manifold Hypothesis for Gradient-Based Explanations
by: Bordt, Sebastian, et al.
Published: (2022)

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models
by: Zheng, Yang, et al.
Published: (2025)

Mind the Gap: Preserving and Compensating for the Modality Gap in CLIP-Based Continual Learning
by: Huang, Linlan, et al.
Published: (2025)

Intriguing Frequency Interpretation of Adversarial Robustness for CNNs and ViTs
by: Chen, Lu, et al.
Published: (2025)