low-dimensional data structure
Low-Dimensional Spaces for Relating Sensor Signals with Internal Data Structure in a Propulsion System
... trends data-driven models produce. In several fields, data is being acquired at an astounding rate ...the data, and helped construct relationships between variables with the algorithm ...of ...
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Sparse and Low-Rank Modeling on High Dimensional Data: A Geometric Perspective.
... Separating data from errors and noise has always been a critical and important problem in signal processing, computer vision and data mining ...recovering low dimensional structures of high ...
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A Study of the Classification of Low-Dimensional Data with Supervised Manifold Learning
... the low-dimensional domain of ...the low-dimensional domain of ...geometric structure must also be preserved so as to ensure that a suffi- ciently regular interpolator can be found to ...
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AUTONOMOUS NETWORK SECURITY FOR UNSUPERVISED DETECTION OF NETWORK ATTACKS
... labeled data to build normal- operation-traffic profiles, detecting anomalies as activities that deviate from this ...multiple low-dimensional spaces. The evidence of traffic structure ...
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Continuum direction vectors in high dimensional low sample size data
... The other extreme case, called HDLSS-asymptotics, emerges rather recently. In Hall et al. (2005), the geometric structure of HDLSS data was explored. They let d go infinity, while keeping the sample size n ...
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Measurements of the three-dimensional structure of flames at low turbulence
... reliable expression for the flame wrinkling scale required in the expression (Lee et al., 2000). The coherent flamelet model (CFM) (Maistret et al., 1989) has features to overcome some of the problems with BML although, ...
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International Journal of Computer Science and Mobile Computing
... a low-dimensional semantic subspace by preserving the relational structure among the documents, but where the mapping between the original document space and the low dimensional ...
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Sparse Linear Discriminant Analysis with Applications to High Dimensional Low Sample Size Data
... the data piling problem. As an illustration of the data piling problem, Figure 1 provides views of two simulated data sets, one of which serves as a training data set, shown in the first row, ...
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Yu, Shipeng (2006): Advanced Probabilistic Models for Clustering and Projection. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik
... discrete data differs from the case on continuous space, where, for example, the most popular technology PCA tries to find the orthogonal dimensions (or factors) that explains the covariance of data ...the ...
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Learning embeddings for multiplex networks using triplet loss
... existing low-dimensional representation learning approaches operate on single-layer networks, but do not necessarily perform well on mul- tiplex ...the structure of each single independently, and ...
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On multi-site damage identification using single-site training data
... transmissibility data were employed as raw data and reduced to low-dimensional features using methods from outlier ...damage data to generalise to multiple-site damage ...the ...
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Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data
... construct low-dimensional geometric approximations of M on an adaptive partition, and provide finite sample performance guarantees for a larger classes of geometric structures M than those considered in ...
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Voronoi classfied and clustered constellation data structure for three-dimensional urban buildings
... with data installation and maintenance, fault tolerance, and low performance during data retrieval and ...3D data will mean that users are confronted with performance issues and this will ...
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Graph Approach Markov Assumptions for Social LDA Inspection
... the low-dimensional structure of data, structure changes over time Unlike other recent work that relies on Markov assumptions of time here each topic is associated with a continuous ...
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Inferring Population Structure and Admixture Proportions in Low-Depth NGS Data
... population structure and admixture proportions in low-depth next-generation sequencing (NGS) ...population structure is essential in both population genetics and association studies, and is often ...
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Low Dimensional Embeddings of Logic
... learn low-dimensional embeddings such that the model behaves as if it follows a com- plex first-order reasoning process—but still oper- ates in terms of simple vector and matrix repre- ...
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A Normalized Tree Index for identification of correlated clinical parameters in microarray experiments
... which is an extension to the study of van’t Veer [7]. For each of the 295 subjects, 24496 genes are analyzed and six nominal clinical parameters are available (Table 1). The clustering of subjects is performed on ...
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Bimetallic MOFs (H3O)x[Cu(MF6)(pyrazine)2]·(4 x)H2O (M = V4+, x = 0; M = Ga3+, x = 1) : co existence of ordered and disordered quantum spins in the V4+ system
... order for both fragments to have the same charge. The computed spin density distributions for the two fragments are shown in Figure 3. The exchange coupling constants were calculated to be AFM (12.6 K) and FM (-0.29 K), ...
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A Generic Multi-Dimensional Run-Time Data Structure for High-Performance Scientific Computing
... the data structure and the target storage ...a data structure once, and then access the already available functionality based on the additional ...
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Low Dimensional Discriminative Reranking
... We use a second order Hidden Markov Model (Thede and Harper, 1999) based tagger as a baseline tagger in our experiments. This model uses trigram transition and emission probabilities and is shown to achieve good ...
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