Deep embedding clustering based on contractive autoencoder

Diallo, Bassoma; Hu, Jie; Li, Tianrui; Khan, Ghufran Ahmad; Liang, Xinyan; Zhao, Yimiao

doi:10.1016/j.neucom.2020.12.094

Cited by 83 publications

(23 citation statements)

References 20 publications

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“…It has been demonstrated that the reconstruction loss can also stabilize the clustering process in terms of the prior knowledge [23]. For sustaining the learned representations to be locally invariant, Diallo et al [50] take on the contractive autoencoder and self-augmentation technique for developing a DC model suited for document datasets. The clustering progress is comparable to DEC except that a similarity measurement is adopted for maintaining the cluster region.…”

Section: Deep Clusteringmentioning

confidence: 99%

Deep clustering with fusion autoencoder

Chang¹

2022

Preprint

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Section: Deep Clusteringmentioning

confidence: 99%

Deep clustering with fusion autoencoder

Chang¹

2022

Preprint

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“…This requires data clustering and outlier analysis to ensure the quality of landmark matching. Clustering algorithms have been extensively studied in recent years [34,35,36,37]. Local Outlier Factor (LOF) [38] can be used to find outliers and remove these matching pairs.…”

Section: 𝐶𝐶𝐶𝐶𝐶𝐶(𝐿𝐿𝐿𝐿_𝐹𝐹 𝐴𝐴mentioning

confidence: 99%

Efficient Visual Place Recognition by Adaptive CNN Landmark Matching

2021

KSII TIIS

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Visual place recognition (VPR) is a fundamental yet challenging task of mobile robot navigation and localization. The existing VPR methods are usually based on some pairwise similarity of image descriptors, so they are sensitive to visual appearance change and also computationally expensive. This paper proposes a simple yet effective four-step method that achieves adaptive convolutional neural network (CNN) landmark matching for VPR. First, based on the features extracted from existing CNN models, the regions with higher significance scores are selected as landmarks. Then, according to the coordinate positions of potential landmarks, landmark matching is improved by removing mismatched landmark pairs. Finally, considering the significance scores obtained in the first step, robust image retrieval is performed based on adaptive landmark matching, and it gives more weight to the landmark matching pairs with higher significance scores. To verify the efficiency and robustness of the proposed method, evaluations are conducted on standard benchmark datasets. The experimental results indicate that the proposed method reduces the feature representation space of place images by more than 75% with negligible loss in recognition precision. Also, it achieves a fast matching speed in similarity calculation, satisfying the real-time requirement.

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“…Among them, the patients who needed Cardiopulmonary Resuscitation (CPR) during the admission to hospital via ER, only 11% survived till the discharge [4]. Among the patients who enter the hospital from Out Patient Department (OPD), it is shown that 80% of the patient (Non-psychological conditions) testimony is fully reliable [7][8]. This proves the point that initial diagnosis is the critical step in the healthcare cycle.…”

Section: Introductionmentioning

confidence: 99%

Processing of Clinical Notes for Efficient Diagnosis with Dual LSTM

S¹,

Seetharam²

2022

IJACSA

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Clinical records contain patient information such as laboratory values, doctor notes, or medications. However, clinical notes are underutilized because notes are complex, highdimensional, and sparse. However, these clinical records may play an essential role in modeling clinical decision support systems. The study aimed to develop an effective predictive learning model that can process these sparse data and extract useful information to benefit the clinical decision support system for the effective diagnosis. The proposed system conducts phasewise data modeling, and suitable text data treatment is carried out for data preparation. The study further utilized the Natural Language Processing (NLP) mechanism where word2vec with Autoencoder is used as a clustering scheme for the topic modeling. Another significant contribution of the proposed work is that a novel approach of learning mechanism is devised by integrating Long Short Term Memory (LSTM) and Convolution Neural Network (CNN) to learn the inter-dependencies of the data sequences to predict diagnosis and patient testimony as output for the clinical decision. The development of the proposed system is carried out using the Python programming language. The study outcome based on the comparative analysis exhibits the effectiveness of the proposed method.

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Deep embedding clustering based on contractive autoencoder

Cited by 83 publications

References 20 publications

Deep clustering with fusion autoencoder

Deep clustering with fusion autoencoder

Efficient Visual Place Recognition by Adaptive CNN Landmark Matching

Processing of Clinical Notes for Efficient Diagnosis with Dual LSTM

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