The high dimensionality of hyperspectral images often results in the degradation of clustering performance. Due to the powerful ability of deep feature extraction and non-linear feature representation, the clustering algorithm based on deep learning has become a hot research topic in the field of hyperspectral remote sensing. However, most deep clustering algorithms for hyperspectral images utilize deep neural networks as feature extractor without considering prior knowledge constraints that are suitable for clustering. To solve this problem, we propose an intra-class distance constrained deep clustering algorithm for high-dimensional hyperspectral images. The proposed algorithm constrains the feature mapping procedure of the auto-encoder network by intra-class distance so that raw images are transformed from the original high-dimensional space to the low-dimensional feature space that is more conducive to clustering. Furthermore, the related learning process is treated as a joint optimization problem of deep feature extraction and clustering. Experimental results demonstrate the intense competitiveness of the proposed algorithm in comparison with state-of-the-art clustering methods of hyperspectral images.
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@cite_64: The detection of anomalous executions is valuable for reducing potential hazards in assistive manipulation. Multimodal sensory signals can be helpful for detecting a wide range of anomalies. However, the fusion of high-dimensional and heterogeneous modalities is a challenging problem for model-based anomaly detection. We introduce a long short-term memory-based variational autoencoder (LSTM-VAE) that fuses signals and reconstructs their expected distribution by introducing a progress-based varying prior. Our LSTM-VAE-based detector reports an anomaly when a reconstruction-based anomaly score is higher than a state-based threshold. For evaluations with 1555 robot-assisted feeding executions, including 12 representative types of anomalies, our detector had a higher area under the receiver operating characteristic curve of 0.8710 than 5 other baseline detectors from the literature. We also show the variational autoencoding and state-based thresholding are effective in detecting anomalies from 17 raw sensory signals without significant feature engineering effort.
@cite_8: Due to the profound differences between acoustic characteristics of neutral and whispered speech, the performance of traditional automatic speech recognition (ASR) systems trained on neutral speech degrades significantly when whisper is applied. In order to deeply analyze this mismatched train test situation and to develop an efficient way for whisper recognition, this study first analyzes acoustic characteristics of whispered speech, addresses the problems of whispered speech recognition in mismatched conditions, and then proposes a new robust cepstral features and preprocessing approach based on deep denoising autoencoder (DDAE) that enhance whisper recognition. The experimental results confirm that Teager-energy-based cepstral features, especially TECCs, are more robust and better whisper descriptors than traditional Mel-frequency cepstral coefficients (MFCC). Further detailed analysis of cepstral distances, distributions of cepstral coefficients, confusion matrices, and experiments with inverse filtering, prove that voicing in speech stimuli is the main cause of word misclassification in mismatched train test scenarios. The new framework based on DDAE and TECC feature, significantly improves whisper recognition accuracy and outperforms traditional MFCC and GMM-HMM (Gaussian mixture density—Hidden Markov model) baseline, resulting in an absolute 31 improvement of whisper recognition accuracy. The achieved word recognition rate in neutral whisper scenario is 92.81 .
@cite_48: Artificial intelligence can learn some concepts by analyzing sensory data similarly to humans. This paper explores how artificial neural networks (ANNs) can learn abstract concepts by analyzing tongue images based on concepts from traditional Chinese medicine (TCM), which is a discipline that relies heavily on practitioner experience. A computer-aided method will be investigated that analyzes sensory data for TCM practitioners. This paper proposes capitalizing on deep learning techniques. A method called the conceptual alignment deep autoencoder (CADAE) is proposed to analyze tongue images that represent different body constitution (BC) types, which are the underlying concepts in TCM. In the first step, CADAE encodes the images to a representation space; in the second step, it decodes the patterns. The experiments demonstrate that CADAE can learn effective representations of abstract concepts aligned with BC types by encoding the tongue images. Furthermore, the representation space of the hidden conceptual neurons can be visualized by a decoder network. The experiments also demonstrate that ANNs acquire different data perspectives when different loss functions are used for training. Numerous representation spaces of ANNs remain to be explored. To some extent, our exploration demonstrates that artificial intelligence (AI) has the ability to learn some concepts in a manner similarly to human beings. Based on this ability, AI shows promise in helping humans form new effective concepts that can facilitate medical development and alleviate the burdens of medical practitioners.
@cite_52: 
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@cite_39: Human pose recovery in videos is usually conducted by matching 2-D image features and retrieving relevant 3-D human poses. In the retrieving process, the mapping between images and poses is critical. Traditional methods assume this mapping relationship as local joint detection or global joint localization, which limits recovery performance of these methods since this two tasks are actually unified. In this paper, we propose a novel pose recovery framework by simultaneously learning the tasks of joint localization and joint detection. To obtain this framework, multiple manifold learning is used and the shared parameter is calculated. With them, multiple manifold regularizers are integrated and generalized eigendecomposition is utilized to achieve parameter optimization. In this way, pose recovery is boosted by both global mapping and local refinement. Experimental results on two popular datasets demonstrates that the recovery error has been reduced by 10 –20 , which proves the performance improvement of the proposed method.
@cite_44: Effective fault diagnosis of rotating machinery has multifarious benefits, such as improved safety, enhanced reliability, and reduced maintenance cost, for complex engineered systems. With many kinds of installed sensors for conducting fault diagnosis, one of the key tasks is to develop data fusion strategies that can effectively handle multimodal sensory signals. Most traditional methods use hand-crafted statistical features and then combine these multimodal features simply by concatenating them into a long vector to achieve data fusion. The present study proposes a deep coupling autoencoder (DCAE) model that handles the multimodal sensory signals not residing in a commensurate space, such as vibration and acoustic data, and integrates feature extraction of multimodal data seamlessly into data fusion for fault diagnosis. Specifically, a coupling autoencoder (CAE) is constructed to capture the joint information between different multimodal sensory data, and then a DCAE model is devised for learning the joint feature at a higher level. The CAE is developed by coupling hidden representations of two single-modal autoencoders, which can capture the joint information from multimodal data. The performance of the proposed method is evaluated by two experiments, which shows that the DCAE model succeeds in efficiently utilizing multisource sensory data to perform accurate fault diagnosis. Compared with other methods, the proposed method exhibits better performance.
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@cite_15: The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.
With impressive learning and characterization capabilities, auto-encoder neural networks have achieved great success in various areas, especially in the scenario of unsupervised learning @cite_15 @cite_63 @cite_3 @cite_62 @cite_52 , such as natural language processing @cite_8 , image processing @cite_48 , object detection @cite_64 , biometric recognition @cite_39 , and data analysis @cite_44 . As state-of-the-art unsupervised techniques, auto-encoder and auto-encoder based neural networks also make outstanding contributions in the field of remote sensing. In this subsection, we briefly introduce the auto-encoder network.