CORE MACHINE LEARNING

Robust Embedded Deep K-means Clustering

November 3, 2020

Abstract

Deep neural network clustering is superior to the conventional clustering methods due to deep feature extraction and nonlinear dimensionality reduction. Nevertheless, deep neural network leads to a rough representation regarding the inherent relationship of the data points. Therefore, it is still difficult for deep neural network to exploit the effective structure for direct clustering. To address this issue, we propose a robust embedded deep K-means clustering (RED- KC) method. The proposed RED-KC approach utilizes the δ -norm metric to constrain the feature mapping process of the auto-encoder network, so that data are mapped to a latent feature space, which is more conducive to the robust clustering. Compared to the existing auto-encoder networks with the fixed prior, the proposed RED-KC is adaptive during the process of feature mapping. More importantly, the proposed RED-KC embeds the clustering process with the auto- encoder network, such that deep feature extraction and clustering can be performed simultaneously. Accordingly, a direct and efficient clustering could be obtained within only one step to avoid the inconvenience of multiple separate stages, namely, losing pivotal information and correlation. Consequently, extensive experiments are provided to validate the effectiveness of the proposed approach.

Download the Paper

AUTHORS

Written by

Rui Zhang

Hanghang Tong

Yinglong Xia

Yada Zhu

Publisher

Facebook AI

Research Topics

Machine Learning

Related Publications

June 14, 2020

Iterative Answer Prediction with Pointer-Augmented Multimodal Transformers for TextVQA | Facebook AI Research

Ronghang Hu, Amanpreet Singh, Trevor Darrell, Marcus Rohrbach

June 14, 2020

April 25, 2020

Permutation Equivariant Models for Compositional Generalization in Language | Facebook AI Research

Jonathan Gordon, David Lopez-Paz, Marco Baroni, Diane Bouchacourt

April 25, 2020

September 15, 2019

SPEECH & AUDIO

Who Needs Words? Lexicon-Free Speech Recognition | Facebook AI Research

Tatiana Likhomanenko, Gabriel Synnaeve, Ronan Collobert

September 15, 2019

September 10, 2019

NLP

Bridging the Gap Between Relevance Matching and Semantic Matching for Short Text Similarity Modeling | Facebook AI Research

Jinfeng Rao, Linqing Liu, Yi Tay, Wei Yang, Peng Shi, Jimmy Lin

September 10, 2019

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.