Cancer subtyping (or cancer subtypes identification) based on multi-omics data has played an important role in advancing diagnosis, prognosis and treatment, which triggers the development of advanced multi-view cluste...
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Cancer subtyping (or cancer subtypes identification) based on multi-omics data has played an important role in advancing diagnosis, prognosis and treatment, which triggers the development of advanced multi-view clustering algorithms. However, the high-dimension and heterogeneity of multiomics data make great effects on the performance of these methods. In this paper, we propose to learn the informative latent representation based on autoencoder (AE) to naturally capture nonlinear omic features in lower dimensions, which is helpful for identifying the similarity of patients. Moreover, to take advantage of survival information or clinical information, a multi-omic survival analysis approach is embedded when integrating the similarity graph of heterogeneous data at the multi-omics level. Then, the clustering method is performed on the integrated similarity to generate subtype groups. In the experimental part, the effectiveness of the proposed framework is confirmed by evaluating five different multi-omics datasets, taken from The Cancer Genome Atlas. The results show that AEassisted multi-omics clustering method can identify clinically significant cancer subtypes.
This paper introduces an algorithm for the detection of change-points and the identification of the corresponding subsequences in transient multivariate time-series data (MTSD). The analysis of such data has become in...
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This paper introduces an algorithm for the detection of change-points and the identification of the corresponding subsequences in transient multivariate time-series data (MTSD). The analysis of such data has become increasingly important due to growing availability in many industrial fields. Labeling, sorting or filtering highly transient measurement data for training Condition-based Maintenance (CbM) models is cumbersome and error-prone. For some applications it can be sufficient to filter measurements by simple thresholds or finding change-points based on changes in mean value and variation. But a robust diagnosis of a component within a component group for example, which has a complex non-linear correlation between multiple sensor values, a simple approach would not be feasible. No meaningful and coherent measurement data, which could be used for training a CbM model, would emerge. Therefore, we introduce an algorithm that uses a recurrent neural network (RNN) based autoencoder (AE) which is iteratively trained on incoming data. The scoring function uses the reconstruction error and latent space information. A model of the identified subsequence is saved and used for recognition of repeating subsequences as well as fast offline clustering. For evaluation, we propose a new similarity measure based on the curvature for a more intuitive time-series subsequence clustering metric. A comparison with seven other state-of-the-art algorithms and eight datasets shows the capability and the increased performance of our algorithm to cluster MTSD online and offline in conjunction with mechatronic systems.
The recent evolution of machine learning (ML) algorithms and the high level of expertise required to use them have fuelled the demand for non-experts solutions. The selection of an appropriate algorithm and the config...
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The recent evolution of machine learning (ML) algorithms and the high level of expertise required to use them have fuelled the demand for non-experts solutions. The selection of an appropriate algorithm and the configuration of its hyperparameters is among the most complicated tasks while applying ML to new problems. It necessitates well awareness and knowledge of ML algorithms. The algorithm selection problem (ASP) is defined as the process of identifying the algorithm (s) that can deliver top performance for a particular problem, task, and evaluation measure. In this context, meta-learning is one of the approaches to achieve this objective by using prior learning experiences to assist the learning process on unseen problems and tasks. As a data-driven approach, appropriate data characterization is of vital importance for the meta-learning. Nonetheless, the recent literature witness a variety of data characterization techniques including simple, statistical and information theory based measures. However, their quality still needs to be improved. In this paper, a new autoencoder-kNN (AeKNN) based meta-model with built-in latent features extraction is proposed. The approach is aimed to extract new characterizations of the data, with lower dimensionality but more significant and meaningful features. AeKNN internally uses a deep autoencoder as a latent features extractor from a set of existing meta-features induced from the dataset. From this new features vectors the computed distances are more significant, thus providing a way to accurately recommending top-performing pipelines for previously unseen datasets. In an application on a large-scale hyperparameters optimization task for 400 real world datasets with varying schemas as a meta-learning task, we show that AeKNN offers considerable improvements of the classical kNN as well as traditional meta-models in terms of performance.
Multimodal medical images have been widely applied in various clinical diagnoses and treatments. Due to the practical restrictions, certain modalities may be hard to acquire, resulting in incomplete data. Existing met...
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Multimodal medical images have been widely applied in various clinical diagnoses and treatments. Due to the practical restrictions, certain modalities may be hard to acquire, resulting in incomplete data. Existing methods attempt to generate the missing data with multiple available modalities. However, the modality differences in tissue contrast and lesion appearance become an obstacle to making a precise estimation. To address this issue, we propose an autoencoder-driven multimodal collaborative learning framework for medical image synthesis. The proposed approach takes an autoencoder to comprehensively supervise the synthesis network using the self-representation of target modality, which provides target-modality-specific prior to guide multimodal image fusion. Furthermore, we endow the autoencoder with adversarial learning capabilities by converting its encoder into a pixel-sensitive discriminator capable of both reconstruction and discrimination. To this end, the generative model is completely supervised by the autoencoder. Considering the efficiency of multimodal generation, we also introduce a modality mask vector as the target modality label to guide the synthesis direction, empowering our method to estimate any missing modality with a single model. Extensive experiments on multiple medical image datasets demonstrate the significant generalization capability as well as the superior synthetic quality of the proposed method, compared with other competing methods.
In this manuscript, we propose an innovative early warning Machine Learning-based model to identify potential threats to financial sustainability for non-financial companies. Unlike most state-of-the-art tools, whose ...
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In this manuscript, we propose an innovative early warning Machine Learning-based model to identify potential threats to financial sustainability for non-financial companies. Unlike most state-of-the-art tools, whose outcomes are often difficult to understand even for experts, our model provides an easily interpretable visualization of balance sheets, projecting each company in a bi-dimensional space according to an autoencoder-based dimensionality reduction matched with a Nearest-Neighbor-based default density estimation. In the resulting space, the distress zones, where the default intensity is high, appear as homogeneous clusters directly identified. Our empirical experiments provide evidence of the interpretability, forecasting ability, and robustness of the bi-dimensional space.
Diffractive deep neural network (D2NN), known for its high speed and strong parallelism, is applied across various fields, including pattern recognition, image processing, and image transmission. However, existing net...
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Diffractive deep neural network (D2NN), known for its high speed and strong parallelism, is applied across various fields, including pattern recognition, image processing, and image transmission. However, existing network architectures primarily focus on data representation within the original domain, with limited exploration of the latent space, thereby restricting the information mining capabilities and multifunctional integration of D2NNs. Here, an all-optical autoencoder (OAE) framework is proposed that linearly encodes the input wavefield into a prior shape distribution in the diffractive latent space (DLS) and decodes the encoded pattern back to the original wavefield. By leveraging the bidirectional multiplexing property of D2NN, the OAE modelsfunction as encoders in one direction and as decoders in the opposite direction. The models are applied to three areas: image denoising, noise-resistant reconfigurable image classification, and image generation. Proof-of-concept experiments are conducted to validate numerical simulations. The OAE framework exploits the potential of latent representations, enabling single set of diffractive processors to simultaneously achieve image reconstruction, representation, and generation. This work not only offers fresh insights into the design of optical generative models but also paves the way for developing multifunctional, highly integrated, and general optical intelligent systems.
Dictionary representations and deep learning autoencoder (AE) models have proven effective in hyperspectral anomaly detection. Dictionary representations offer self-explanation but struggle with complex scenarios. Con...
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Dictionary representations and deep learning autoencoder (AE) models have proven effective in hyperspectral anomaly detection. Dictionary representations offer self-explanation but struggle with complex scenarios. Conversely, autoencoders can capture details in complex scenes but lack self-explanation. Complex scenarios often involve extensive spatial information, making its utilization crucial in hyperspectral anomaly detection. To effectively combine the advantages of both methods and address the insufficient use of spatial information, we propose an attention constrained low-rank and sparse autoencoder for hyperspectral anomaly detection. This model includes two encoders: an attention constrained low-rank autoencoder (AClrAE) trained with a background dictionary and incorporating a Global Self-Attention Module (GAM) to focus on global spatial information, resulting in improved background reconstruction;and an attention constrained sparse autoencoder (ACsAE) trained with an anomaly dictionary and incorporating a Local Self-Attention Module (LAM) to focus on local spatial information, resulting in enhanced anomaly reconstruction. Finally, to merge the detection results from both encoders, a nonlinear fusion scheme is employed. Experiments on multiple real and synthetic datasets demonstrate the effectiveness and feasibility of the proposed method.
Although existing graph self-supervised learning approaches have paid attention to the directed nature of networks, they have often overlooked the ubiquitous scale-free attributes. This oversight has resulted in a the...
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Although existing graph self-supervised learning approaches have paid attention to the directed nature of networks, they have often overlooked the ubiquitous scale-free attributes. This oversight has resulted in a theoretical gap in understanding graph self-supervised learning from the perspective of network structure. In this paper, we study the degree distribution characteristics of source and target nodes in directed scale-free networks, encompassing node and edge dimensions. Our theoretical analysis reveals the relationship between the average degree of nodes and their average in-degree and out-degree, which is instrumental in discerning positive and negative edges, as well as edge directionality. Here positive edges are the ones that exists in the original graph, and negative edges are the ones that not exists in the original graph. Furthermore, we uncover negative edges connecting to central nodes and positive edges to peripheral nodes to be less predictable. Based on these crucial theoretical insights, we propose DMGAE (Directed Masked Graph autoencoder), a novel representation learning method for directed scale-free networks that offers interpretability. The DMGAE method employs a weighted graph based on edges to replace the original graph structure. It integrates a masking approach based on the weight of the edges. Additionally, it incorporates an adaptive negative sampling method, edge decoder and a degree decoder based on the difference between the in- degree and out-degree of the node. This enhances the model's capability to learn edges and discern their directions. Empirical studies on extensive real-world network data show that, compared to the state-of-the-art methods, DMGAE not only generally has superior learning performance on directed networks, but also performs exceedingly well on undirected networks.
Nowadays,the personalized recommendation has become a research hotspot for addressing information *** this,generating effective recommendations from sparse data remains a ***,auxiliary information has been widely used...
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Nowadays,the personalized recommendation has become a research hotspot for addressing information *** this,generating effective recommendations from sparse data remains a ***,auxiliary information has been widely used to address data sparsity,but most models using auxiliary information are linear and have limited *** to the advantages of feature extraction and no-label requirements,autoencoder-based methods have become quite ***,most existing autoencoder-based methods discard the reconstruction of auxiliary information,which poses huge challenges for better representation learning and model *** address these problems,we propose Serial-autoencoder for Personalized Recommendation(SAPR),which aims to reduce the loss of critical information and enhance the learning of feature ***,we first combine the original rating matrix and item attribute features and feed them into the first autoencoder for generating a higher-level representation of the ***,we use a second autoencoder to enhance the reconstruction of the data representation of the prediciton rating *** output rating information is used for recommendation *** experiments on the MovieTweetings and MovieLens datasets have verified the effectiveness of SAPR compared to state-of-the-art models.
Sound and vibration analysis are prominent tools for machine health diagnosis. Especially, neural network (NN) strategies have focused on finding complex and nonlinear relationships between the sensor signal and the m...
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Sound and vibration analysis are prominent tools for machine health diagnosis. Especially, neural network (NN) strategies have focused on finding complex and nonlinear relationships between the sensor signal and the machine status to detect machine faults. However, it is difficult to collect enough amount of fault data as much as normal status data for training general NN models. To resolve the issue, this paper proposes the autoencoder-based anomaly detection framework for industrial robot arms using an internal sound sensor. The autoencoder uses signals in the normal state of the robots for training the model. It reconstructs the input signals as output, and anomalous states are found from high reconstruction error. Two stethoscopes were attached to the surface of the robot joint as sensors, and the sounds were recorded by USB microphone attached to the outlet of the stethoscopes. Features were extracted from STFT spectrogram images of the gathered sound, then used to train and test an autoencoder model. The reconstruction errors of the autoencoder were compared to distinguish the abnormal status from normal one. The experimental results suggest that the stethoscopes prevent the interference of noise, and the collected sound signals can be utilized for detecting machine anomalies.
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