To this end, we propose the deep-stacked sparse embedded clustering method in this paper, which considers both the preservation of local structure and sparse property of inputs. The proposed network is trained t
In this study, we proposed a new method for feature extraction using a stacked sparse autoencoder to extract the discriminative features from the unlabeled data of breath samples. A Softmax classifier was then integrated to the proposed method of feature extraction, to classify gastric cancer from...
ScAnCluster can do both intra-dataset and inter-dataset cell clustering and annotation, and it also reveals a clear discriminatory effect in the detection of new cell types not found in the reference [157]. Tian et al. created the Single-Cell model-based Deep embedded Clustering (scDeep...
Improved deep convolutional embedded clustering with re-selectable sample training 2022, Pattern Recognition Citation Excerpt : For example, we can apply the model to medical image processing and other applications [38,39]. Show abstract Functional magnetic resonance imaging, deep learning, and Alzheimer...
Efficient Deep Embedded Subspace Clustering Jinyu Cai1,3, Jicong Fan2,3∗, Wenzhong Guo1, Shiping Wang1, Yunhe Zhang1, Zhao Zhang4 1College of Computer and Data Science, Fuzhou University, China 2School of Data Science, The Chinese University of Hong Kong (Shenzhen), China 3Shenzhen ...
We scaled the mean and the variance of the weight matrices and biases in the consecutive layers where both networks are stacked. (4) Finding the disease scores from the expression: The absolute value of scores sp, for prioritizing the genes related to the pth phenotype are computed by the ...
The embedded features vector The adjacency matrix The attributed matrix The proximity matrix The consensus matrix The representation loss The clustering loss G V E T n k Z A 2 RnÂn T 2 RþnÂt P 2 RþnÂt C 2 RnÂn Lrep Lclu attribute fits a unique multi-dimensional schema....
StereoVAE: "StereoVAE: A lightweight stereo-matching system using embedded GPUs", Chang et al., ICRA, 2023. [Paper] [Bibtex] [Google Scholar] ADStereo: "ADStereo: Efficient Stereo Matching With Adaptive Downsampling and Disparity Alignment", Wang et al., TIP, 2023. [Paper] [Code] [Bib...
A revisit of sparse coding based anomaly detection in stacked RNN framework. ICCV, 2017. paper Weixin Luo, Wen Liu, and Shenghua Gao. The MVTec anomaly detection dataset: A comprehensive real-world dataset for unsupervised anomaly detection. IJCV, 2021. paper Paul Bergmann, Kilian Batzner, Micha...
Machine Learning(ML) is a subset of AI techniques that enables computer systems to learn from previous experience (i.e. data observations) and improve their behaviour for a given task. ML techniques include Support Vector Machines (SVM), decision trees, Bayes learning, k-means clustering, associ...