Data-Based Subspace Learning
Moving Object Detection Based on Subspace Learning
... machine learning and pattern recognition, moving object de- tection has always been a popular research direction, and has received extensive attention of academia and ...hand, subspace learning is a ...
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Query-driven learning for predictive analytics of data subspace cardinality
... emerging data en- vironments where data accesses are undesirable or ...model based on unsupervised regression inte- grated with supervised vector ...operating data nodes’ local models, which ...
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Grassmann Discriminant Analysis: a Unifying View on Subspace-Based Learning
... discriminant learning framework for problems in which data consist of linear subspaces instead of ...make learning algorithms adapt naturally to the problems with lin- ear invariant ...the ...
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A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data
... fuzzy based backward elimination method is used for removing inap- propriate genes from the independent component feature vector and the termination criterion in our method is based on the classi fi cation ...
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Robust online subspace learning
... incremental learning system to find slow features is required for detecting changes in video ...is based on statistical efficiency and incrementally estimates the data distribution by means of scale ...
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Subspace Learning with Partial Information
... of subspace learning with partial information is based on the matrix completion ...a data matrix with unobserved ...the data matrix) should scale with the rank of the data matrix ...
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Robust Subspace Segmentation by Simultaneously Learning Data Representations and Their Affinity Matrix
... of subspace segmentation is to partition a set of data drawn from a union of subspace into their underlying ...clustering based approaches heavily de- pends on learned data affinity ...
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Multi-view Subspace Learning for Large-Scale Multi-Modal Data Analysis
... of data in the modern world dictates the development of new technologies for processing and analysing it, giving rise to the field of machine ...machine learning has been applied to solve problems in many ...
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Multi-Task Learning for Subspace Segmentation
... Abstract Subspace segmentation is the process of cluster- ing a set of data points that are assumed to lie on the union of multiple linear or affine subspaces, and is increasingly being recognized as a fun- ...
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Multiple Feature Fusion by Subspace Learning
... multimedia data, feature fusion has been more and more important for image and video retrieval, indexing and ...analysis based methods for joint dimensionality reduction in the feature ...general ...
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Singing speaker clustering based on subspace learning in the GMM mean supervector space
... PCA subspace learning prior to speaker clustering renders approximately similar performance to the baseline system, while LPP subspace learning improves the clustering accuracies up to ...the ...
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Low Rank Sample Reconstruction-based Semi-supervised. Feature Subspace Learning
... conventional subspace learning models are supervised，the data containing only partial labeled samples will lead to unsatisfactory classification results in practical ...feature subspace model ...
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Exploiting saliency information in discriminant subspace learning
... sional data can be mapped into the discriminant subspace for ...classes based on the scatter of these classes representations with respect to the total data ...
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Subspace learning from image gradient orientations
... Subspace Learning from Image Gradient Orientations Georgios Tzimiropoulos, Member, IEEE, Stefanos Zafeiriou Member, IEEE, and Maja Pantic Fellow, IEEE Abstract—We introduce the notion of subspace ...
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Perceptual Audio Source Separation By Subspace Learning
... mixtures, based on the IS and the KL divergences, ...method based on Gabor filtering of primary cortical representation of speech signals and tensor factorization is ...of data samples on the tensor ...
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Minimal Sample Subspace Learning: Theory and Algorithms
... 4. Based on these theoretical anal- yses, we model the MSS problem as a computational optimization problem in Section 5, covering a closed form of representation matrices, the connectivity of diagonal blocks, ...
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Blockchain and Random Subspace Learning-based IDS for SDN-enabled Industrial IoT Security
... necessary data along with traditional defense mechanisms like firewalls and ...machine learning-based big data processing technologies for anomaly detection [ 20 ...traffic data that is ...
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Connecting Subspace Learning and Extreme Learning Machine in Speech Emotion Recognition
... Connecting Subspace Learning and Extreme Learning Machine in Speech Emotion Recognition Xinzhou Xu, Jun Deng, Eduardo Coutinho, Chen Wu, Li Zhao, and Bj¨orn Schuller, Fellow, IEEE Abstract—Speech ...
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HIGH DIMENSIONAL DATA WITH SUBSPACE AND OUTLIER ANALYSIS USING MODEL BASED CLUSTERING ALGORITHM
... Anomaly data values are verified with similarity under the clustering ...The subspace selection process is ...a data set into subsets, so that the data in each subset share some common trait - ...
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Learning distance to subspace for the nearest subspace methods in high-dimensional data classification
... Nearest subspace methods (NSM) are a category of classification methods widely applied to classify high-dimensional ...through learning tailored distance metrics from samples to class ...to subspace’ ...
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