Deep Clustering with Convolutional Autoencoders

Guo, Xifeng; Liu, Xinwang; Zhu, En; Yin, Jianping

doi:10.1007/978-3-319-70096-0_39

Cited by 419 publications

(385 citation statements)

References 8 publications

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“…In our unsupervised HSI segmentation approach (3D-CAE) inspired 2 by [19], we exploit 3D convolutional autoencoders (CAEe) to extract deep features which later undergo clustering ( Fig. 1).…”

Section: Unsupervised Hsi Segmentation Using 3d Convolutional Autmentioning

confidence: 99%

Unsupervised Segmentation of Hyperspectral Images Using 3-D Convolutional Autoencoders

Nalepa

Myller

Imai

et al. 2020

IEEE Geosci. Remote Sensing Lett.

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Hyperspectral image analysis has become an important topic widely researched by the remote sensing community. Classification and segmentation of such imagery help understand the underlying materials within a scanned scene, since hyperspectral images convey a detailed information captured in a number of spectral bands. Although deep learning has established the state of the art in the field, it still remains challenging to train well-generalizing models due to the lack of ground-truth data. In this letter, we tackle this problem and propose an end-to-end approach to segment hyperspectral images in a fully unsupervised way. We introduce a new deep architecture which couples 3D convolutional autoencoders with clustering. Our multi-faceted experimental study-performed over benchmark and real-life data-revealed that our approach delivers high-quality segmentation without any prior class labels.

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Section: Unsupervised Hsi Segmentation Using 3d Convolutional Autmentioning

confidence: 99%

Unsupervised Segmentation of Hyperspectral Images Using 3-D Convolutional Autoencoders

Nalepa

Myller

Imai

et al. 2020

IEEE Geosci. Remote Sensing Lett.

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“…Reducing the dimensionality is achieved by unsupervised training of an encoder and a decoder neural network, minimizing the reconstruction error [19,32]. The latent features resulting from the encoder are flattened, and one of PCA, UMAP, or t-SNE is then used to reduce them to 2D for visualization.…”

Section: Deep Convolutional Auto-encoder (Dcae)mentioning

confidence: 99%

“…DCAE aims to find a code for each input by minimizing the mean squared error (MSE) between its input (original data) and output (reconstructed data). The MSE is used which assists to minimize the loss; thus, the network is forced to learn a low-dimensional representation of the input [14,19]. For convenience, all layers input and output shape, filters size, number of kernels, number of units, and activation functions of the DCAE are summarized in Table 1 and can be explained as detailed below:…”

Section: Architecturementioning

confidence: 99%

TimeCluster: dimension reduction applied to temporal data for visual analytics

et al. 2019

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There is a need for solutions which assist users to understand long time-series data by observing its changes over time, finding repeated patterns, detecting outliers, and effectively labeling data instances. Although these tasks are quite distinct and are usually tackled separately, we present an interactive visual analytics system and approach that can address these issues in a single system. It enables users to visualize, understand and explore univariate or multivariate long time-series data in one image using a connected scatter plot. It supports interactive analysis and exploration for pattern discovery and outlier detection. Different dimensionality reduction techniques are used and compared in our system. Because of its power of extracting features, deep learning is used for multivariate time-series along with 2D reduction techniques for rapid and easy interpretation and interaction with large amount of time-series data. We deploy our system with different time-series datasets and report two real-world case studies that are used to evaluate our system.

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“…Such is the case for the confusion generated amongst algorithms caused by sedimentary onlaps, causing fossiliferous levels to lie closer to each other, at both Batallones-3 and Batallones-10. This may, however, be solved by more complex AIAs and the use of Deep MT systems, such as clustering AIAs using auto-encoders (Xie, Girshick & Farhadi, 2016;Guo et al, 2017;Mrabah et al, 2019;Yang et al, 2019), or those used for reinforcement learning tasks (Lake et al, 2014;Mnih et al, 2015;Holzinger, 2016;Simard et al, 2017). Efforts should therefore be made to investigate the effects of these numerous geological components on pattern detection algorithms.…”

Section: Discussionmentioning

confidence: 99%

Application of artificially intelligent systems for the identification of discrete fossiliferous levels

Martín-Perea

Courtenay

Domingo

et al. 2020

PeerJ

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The separation of discrete fossiliferous levels within an archaeological or paleontological site with no clear stratigraphic horizons has historically been carried out using qualitative approaches, relying on two-dimensional transversal and longitudinal projection planes. Analyses of this type, however, can often be conditioned by subjectivity based on the perspective of the analyst. This study presents a novel use of Machine Learning algorithms for pattern recognition techniques in the automated separation and identification of fossiliferous levels. This approach can be divided into three main steps including: (1) unsupervised Machine Learning for density based clustering (2) expert-in-the-loop Collaborative Intelligence Learning for the integration of geological data followed by (3) supervised learning for the final fine-tuning of fossiliferous level models. For evaluation of these techniques, this method was tested in two Late Miocene sites of the Batallones Butte paleontological complex (Madrid, Spain). Here we show Machine Learning analyses to be a valuable tool for the processing of spatial data in an efficient and quantitative manner, successfully identifying the presence of discrete fossiliferous levels in both Batallones-3 and Batallones-10. Three discrete fossiliferous levels have been identified in Batallones-3, whereas another three have been differentiated in Batallones-10.

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Deep Clustering with Convolutional Autoencoders

Cited by 419 publications

References 8 publications

Unsupervised Segmentation of Hyperspectral Images Using 3-D Convolutional Autoencoders

Unsupervised Segmentation of Hyperspectral Images Using 3-D Convolutional Autoencoders

TimeCluster: dimension reduction applied to temporal data for visual analytics

Application of artificially intelligent systems for the identification of discrete fossiliferous levels

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