36 citations · 61 across the 5 of their papers we have counts for
5 papers
Deep Distance Measurement Method for Unsupervised Multivariate Time Series Similarity Retrieval
Susumu Naito, Kouta Nakata, Yasunori Taguchi
We propose the Deep Distance Measurement Method (DDMM) to improve retrieval accuracy in unsupervised multivariate time series similarity retrieval. DDMM enables learning of minute…
Clustering-friendly Representation Learning for Enhancing Salient Features
Toshiyuki Oshima, Kentaro Takagi, Kouta Nakata
Recently, representation learning with contrastive learning algorithms has been successfully applied to challenging unlabeled datasets. However, these methods are unable to disting…
Anomaly Detection for Multivariate Time Series on Large-scale Fluid Handling Plant Using Two-stage Autoencoder
Susumu Naito, Yasunori Taguchi, Kouta Nakata +1
This paper focuses on anomaly detection for multivariate time series data in large-scale fluid handling plants with dynamic components, such as power generation, water treatment, a…
Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation
Yaling Tao, Kentaro Takagi, Kouta Nakata
Clustering is one of the most fundamental tasks in machine learning. Recently, deep clustering has become a major trend in clustering techniques. Representation learning often play…
RDEC: Integrating Regularization into Deep Embedded Clustering for Imbalanced Datasets
Yaling Tao, Kentaro Takagi, Kouta Nakata
Clustering is a fundamental machine learning task and can be used in many applications. With the development of deep neural networks (DNNs), combining techniques from DNNs with clu…