Improved Deep Embedded Clustering with Local Structure Preservation
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Summary
The Improved Deep Embedded Clustering (IDEC) algorithm can jointly optimize cluster labels assignment and learn features that are suitable for clustering with local structure preservation by integrating the clustering loss and autoencoder’s reconstruction loss.
- Type
- article
- Published
- 2017-08-01
- Cited by
- 1,061
- References
- 25
- Access
- Open access
- OpenAlex
- https://openalex.org/W2741943936
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:39311659
Keywords
Cluster analysis, Autoencoder, Computer science, Artificial intelligence, Feature (linguistics)
References
- Deep Learning with Nonparametric Clustering
- Pattern Recognition and Machine Learning
- Analyzing the effectiveness and applicability of co-training
- Gradient-based learning applied to document recognition
- Some methods for classification and analysis of multivariate observations
- Spectral Embedded Clustering: A Framework for In-Sample and Out-of-Sample Spectral Clustering
- A tutorial on spectral clustering
- Recognizing linked events: Searching the space of feasible explanations
- RCV1: A New Benchmark Collection for Text Categorization Research
- Deep Sparse Rectifier Neural Networks
- Visualizing Data using t-SNE
- Joint Unsupervised Learning of Deep Representations and Image Clusters
- Learning Deep Representations for Graph Clustering
- Variational Deep Embedding: A Generative Approach to Clustering
- Deep Subspace Clustering with Sparsity Prior
- Some methods for classi cation and analysis of multivariate observations
- Et al
- Deep Learning
- Adam: A Method for Stochastic Optimization
- Unsupervised Deep Embedding for Clustering Analysis
Cited by
- Graph Clustering with Dynamic Embedding
- Bridging Text Visualization and Mining: A Task-Driven Survey
- ClusterNet : Semi-Supervised Clustering using Neural Networks
- Semi-Supervised Clustering with Neural Networks
- Improving Image Clustering With Multiple Pretrained CNN Feature Extractors
- Deep k-Means: Jointly Clustering with k-Means and Learning Representations
- Ensemble Clustering via Learning Representations from Auto-Encoder
- Multilingual Clustering of Streaming News
- Improved image clustering with deep semantic embedding
- Point Symmetry-based Deep Clustering
- Semi-supervised deep embedded clustering
- Deep Neural Maps
- Recurrent Deep Divergence-based Clustering for Simultaneous Feature Learning and Clustering of Variable Length Time Series
- Deep collective matrix factorization for augmented multi-view learning
- Deep Density-based Image Clustering
- Estimating Rationally Inattentive Utility Functions with Deep Clustering for Framing - Applications in YouTube Engagement Dynamics
- Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering
- Data-Driven Representative Day Selection for Investment Decisions: A Cost-Oriented Approach
- Spectral Clustering via Ensemble Deep Autoencoder Learning (SC-EDAE)
- Deep Constrained Clustering - Algorithms and Advances
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