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Unsupervised Learning

Evaluate then Cooperate: Shapley-based View Cooperation Enhancement for Multi-view Clustering
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Machine Learning Unsupervised Learning 🏢 National University of Defence Technology
Shapley-based Cooperation Enhancing Multi-view Clustering (SCE-MVC) improves deep multi-view clustering by using game theory to fairly evaluate and enhance individual view contributions.
Dissect Black Box: Interpreting for Rule-Based Explanations in Unsupervised Anomaly Detection
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Machine Learning Unsupervised Learning 🏢 Tsinghua University
SCD-Tree & GBD: Unlocking interpretable rules for unsupervised anomaly detection!
Controlling Continuous Relaxation for Combinatorial Optimization
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Machine Learning Unsupervised Learning 🏢 Fujitsu Limited
Continuous Relaxation Annealing (CRA) significantly boosts unsupervised learning-based solvers for combinatorial optimization by dynamically shifting from continuous to discrete solutions, eliminating…
Clustering with Non-adaptive Subset Queries
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Machine Learning Unsupervised Learning 🏢 UC San Diego
This paper introduces novel non-adaptive algorithms for clustering using subset queries, achieving near-linear query complexity and improving upon existing limitations of pairwise query methods.
Cascade of phase transitions in the training of energy-based models
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Machine Learning Unsupervised Learning 🏢 Université PSL
Energy-based models’ training reveals a cascade of phase transitions, progressively learning data features, offering new insights into deep learning dynamics.
Capturing the denoising effect of PCA via compression ratio
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Machine Learning Unsupervised Learning 🏢 Computer Science, University of Southern California
PCA’s denoising effect is quantified via a novel metric: compression ratio. This metric reveals PCA’s ability to reduce intra-community distances while preserving inter-community distances in noisy d…
Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learning
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Machine Learning Unsupervised Learning 🏢 University of Electronic Science and Technology of China
InfoMGF, a novel framework, tackles the limitations of unsupervised multiplex graph learning by refining graph structures, maximizing task-relevant information (both shared and unique), and achieving …
Approximately Pareto-optimal Solutions for Bi-Objective k-Clustering
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AI Generated Machine Learning Unsupervised Learning 🏢 Heinrich Heine University Düsseldorf
This paper presents novel algorithms for approximating Pareto-optimal solutions to bi-objective k-clustering problems, achieving provable approximation guarantees and demonstrating effectiveness throu…
Alleviate Anchor-Shift: Explore Blind Spots with Cross-View Reconstruction for Incomplete Multi-View Clustering
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Machine Learning Unsupervised Learning 🏢 National University of Defense Technology
AIMC-CVR: A novel approach that alleviates anchor-shift in incomplete multi-view clustering via cross-view reconstruction, improving accuracy and scalability.
Aligning Embeddings and Geometric Random Graphs: Informational Results and Computational Approaches for the Procrustes-Wasserstein Problem
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Machine Learning Unsupervised Learning 🏢 DI ENS, CRNS, PSL University, INRIA Paris
This paper presents novel informational results and a new algorithm (‘Ping-Pong’) for solving the Procrustes-Wasserstein problem, significantly advancing high-dimensional data alignment.