Paper tackles indoor localization issues with reduced dimensionality and improved fingerprint matching.
problem Curse of dimensionality and asymmetric matching in fingerprint-based indoor localization.
method Proposes a semi-supervised RSS dimensionality reduction algorithm and integrates it with a fingerprint-based algorithm.
result Improves indoor localization accuracy by reducing dimensionality and addressing matching issues.
Paper presents a new Wi-Fi RSS and geomagnetic field database for indoor localization and trajectory estimation.
problem Indoor localization and trajectory estimation challenges.
method Convolutional neural network (CNN) for RSS data and LSTM network for geomagnetic field intensity.
result CNN and LSTM networks show feasibility for localization and trajectory estimation.
Proposes Siegel neural networks for improved classification tasks.
problem Classification on Siegel spaces is underexplored.
method Uses quotient structure and vector-valued distance notation.
result Demonstrates state-of-the-art performance in radar and node classification.
New method uses SVBI to quickly generate radio maps for indoor positioning.
problem Time-consuming site surveys for creating radio maps in large indoor spaces.
method Combines fingerprinting positioning and SVBI for efficient radio map generation.
result Significantly reduces the cost and time of creating radio maps for indoor positioning.
Paper proposes DRL for unsupervised IoT localization.
problem Challenges in unsupervised localization of IoT devices.
method Modeling localization as MDP, using DRL with reward-setting and RSS measurements.
result Demonstrated effectiveness of DRL in wireless localization.
WiFi helps align and calibrate foot-mounted IMU trajectories.
problem Inertial drift and unknown initial states in FMIP.
method Graph-based SLAM with RSS measurements for WiFi APs.
result Aligns and calibrates trajectories accurately.
RecSim creates customizable simulation environments for RSs.
problem Creating realistic simulation environments for RSs.
method Configurable platform for authoring simulation environments.
result Enables pushing the limits of RL and RS techniques.
A multiple classifiers fusion localization technique using received signal strengths (RSSs) of visible light is proposed, in which the proposed system transmits different intensity modulated sinusoidal signals by LEDs and the signals received by a Photo Diode (PD) placed at various grid points. First, we obtain some {\…
New proof shows random neural networks contain sparse subnetworks.
problem Finding sparse subnetworks in random neural networks.
method Proof of the Strong Lottery Ticket Hypothesis using the Random Fixed-Size Subset Sum Problem.
result Guaranteed sparsity of subnetworks in random neural networks.
Noise-ignorant empirical risk minimization achieves state-of-the-art performance on noisy data.
problem Learning with noisy labels in multi-class classification problems.
method Introducing relative signal strength (RSS) to quantify transferability and applying Noise Ignorant Empirical Risk Minimization (NI-ERM).
result NI-ERM achieves state-of-the-art performance on CIFAR-N data challenge.
Proposes a mathematical model for safe and scalable self-driving cars.
problem Ensuring safety and scalability in self-driving cars.
method Introduces Responsibility-Sensitive Safety (RSS) model and scalable design.
result Proposes a mathematical model for safety assurance and scalable design.
Paper presents an energy-efficient RL method for sensor networks.
problem Energy consumption in sensor networks for health monitoring.
method Adaptive Reinforcement Learning framework using SARSA algorithm.
result Achieves performance enhancement and energy savings over time.
DeepPos uses deep learning to improve indoor location accuracy.
problem Indoor GPS accuracy issues with mobile devices.
method Supervised autoencoder network for modeling CSI-based environments.
result Robust and efficient indoor localization system developed.
Gradient optimization improves preference elicitation for large item spaces.
problem Computational infeasibility of EVOI for large item spaces in recommender systems.
method Continuous formulation of EVOI as a differentiable network, optimized using gradient methods.
result Gradient-based EVOI optimization achieves state-of-the-art performance and scalability.
Most existing fingerprints-based indoor localization approaches are based on some single fingerprints, such as received signal strength (RSS), channel impulse response (CIR), and signal subspace. However, the localization accuracy obtained by the single fingerprint approach is rather susceptible to the changing environ…
New job recommendation system improves job seekers' welfare through field experiments.
problem Current job recommendation systems focus on clicks and applications, not job seekers' welfare.
method Developed a job-search model with two dimensions: utility and success probability. Conducted field experiments to validate model predictions.
result Welfare-optimal job recommendation algorithms outperform existing approaches and perform close to the benchmark.
The Lagrange regularisation method detects financial bubbles' start times objectively.
problem Identifying the start time of financial bubbles.
method Lagrange regularisation of the normalised sum of squared residuals.
result The method provides well-defined determinations of bubble start times.
Quantum analysis tags news for sentiment and entities.
problem Identifying bias in news reporting.
method Continuous data collection, NER and sentiment analysis.
result A corpus of tagged news articles for public use.
We study contact structures compatible with genus one open book decompositions with one boundary component. Any monodromy for such an open book can be written as a product of Dehn twists around dual non-separating curves in the once-punctured torus. Given such a product, we supply an algorithm to determine whether the …
Paper reviews and synthesizes methods for evaluating dimensionality reduction techniques.
problem Evaluating and comparing dimensionality reduction techniques.
method Framework and toolkit in R for exploring and evaluating dimensionality reduction quality through visual insights.
result Helps researchers compare and select dimensionality reduction techniques using visual insights.
Paper compares dimensionality reduction methods for affect classification.
problem Difficulty in obtaining labeled training samples for affect classification.
method Five dimensionality reduction approaches are compared.
result No single approach universally outperforms others in affect classification.
Study explores dimensionality reduction ensembles for unsupervised learning.
problem Lack of successful ensemble methods in unsupervised learning, particularly dimensionality reduction.
method Combines principal component analysis and manifold learning techniques to capture various features.
result Dimensionality reduction ensembles improve classifier accuracy on real medical datasets.
The paper evaluates and compares dimensionality reduction quality metrics without tuning.
problem Evaluating the quality of nonlinear dimensionality reduction visualizations is challenging.
method Comparison of dimensionality reduction quality metrics on datasets with known ground truth manifolds.
result A few methods consistently perform well, with one proposed as a benchmark.
A method constructs a stochastic surrogate from dimensionality reduction results for high-dimensional uncertainty quantification.
problem High-dimensional uncertainty quantification with physics-based models.
method Constructs a stochastic surrogate model from dimensionality reduction results.
result Preserves convenience of sequential dimensionality reduction and Gaussian process regression while overcoming limitations.
Unified framework for DR and clustering using Gromov-Wasserstein.
problem Capturing structure in high-dimensional datasets.
method Distributional reduction framework using Gromov-Wasserstein.
result Unified approach recovers DR and clustering as special cases.
Adapts DR objectives for both sample and feature size reduction.
problem Simultaneously reduce sample and feature sizes.
method Semi-relaxed Gromov-Wasserstein optimal transport.
result OT plan delivers competitive hard clustering.
New DR method uses Gromov-Wasserstein distance for high-dimensional data.
problem Analyzing relationships between high-dimensional objects.
method Optimal transportation theory and Gromov-Wasserstein distance.
result Robust and efficient solution for complex high-dimensional datasets.
Classifies 7- and 8-dimensional naturally reductive spaces.
problem Classifying naturally reductive spaces in 7 and 8 dimensions.
method Combines structure theory and new construction methods.
result Complete classification of 7- and 8-dimensional naturally reductive spaces.
This report concerns the problem of dimensionality reduction through information geometric methods on statistical manifolds. While there has been considerable work recently presented regarding dimensionality reduction for the purposes of learning tasks such as classification, clustering, and visualization, these method…
Paper proposes a novel R-JDRDL method for SPD manifolds.
problem High-dimensional noisy signals analysis.
method Riemannian optimization framework for joint DR and DL.
result R-JDRDL outperforms existing algorithms in image classification.
Survey on geometric foundations of data reduction methods.
problem High-dimensional data with intrinsic nonlinear structure.
method Spectral manifold learning methods.
result Derivation and convergence analysis of spectral manifold learning.
SDSPCAAN combines supervised and local data structures for better dimensionality reduction.
problem Preserving both global and local data structures for noisy high-dimensional data.
method Supervised discriminative sparse PCA with adaptive neighbors (SDSPCAAN).
result SDSPCAAN improves classification accuracy on high-dimensional datasets.
We classify non-reductive four-dimensional homogeneous conformally Einstein manifolds.
Survey of SDR methods for high-dimensional regression and embedding.
problem Reducing dimensionality in high-dimensional data.
method Involves both statistical and machine learning approaches, covering inverse and forward regression methods.
result Supervised Kernel Dimension Reduction is equivalent to supervised PCA.
Modeling data as being sampled from a union of independent subspaces has been widely applied to a number of real world applications. However, dimensionality reduction approaches that theoretically preserve this independence assumption have not been well studied. Our key contribution is to show that 2K projection vect…
Dimensionality reduction methods are very common in the field of high dimensional data analysis. Typically, algorithms for dimensionality reduction are computationally expensive. Therefore, their applications for the analysis of massive amounts of data are impractical. For example, repeated computations due to accumula…
Reduces complexity of financial contagion dynamics on networks.
problem Complexity of financial contagion dynamics on networks.
method Dimensional reduction methods (degree-weighted and spectral reduction).
result Spectral reduction better handles heterogeneous networks.
This study explores Kaluza-Klein reductions of new maximally supersymmetric backgrounds.
problem Exploring new maximally supersymmetric backgrounds in five dimensions.
method Classifying Kaluza-Klein reductions to four dimensions and determining preserved supersymmetry.
result Discovery of novel non-homogeneous four-dimensional Lorentzian spacetimes with N=1 supersymmetry. In this paper we show that for the purposes of dimensionality reduction certain class of structured random matrices behave similarly to random Gaussian matrices. This class includes several matrices for which matrix-vector multiply can be computed in log-linear time, providing efficient dimensionality reduction of gene…
UMAP compared to other methods for dimensionality reduction.
problem Comparing UMAP to other dimensionality reduction techniques.
method Comprehensive evaluation of UMAP and other methods.
result Supervised UMAP performs well for classification but not for regression.
Paper presents a new method for reducing data dimensions on graphs.
problem High-dimensional datasets in signal processing and machine learning.
method Nonlinear dimensionality reduction framework for graphs.
result Framework captures nonlinear correlations and multiple graphs.
The classification of 4-dimensional naturally reductive pseudo-Riemannian spaces is given. This classification comprises symmetric spaces, the product of 3-dimensional naturally reductive spaces with the real line and new families of indecomposable manifolds which are studied at the end of the article. The oscillator g…
Paper reduces movement primitive dimensionality in parameter space.
problem High dimensionality of movement primitives makes policy optimization expensive.
method Investigates dimensionality reduction in parameter space, identifying principal movements.
result Dimensionality reduction in parameter space is more effective than in configuration space.
Detects adversarial examples with non-linear dimensionality reduction.
problem Vulnerability of deep neural networks to adversarial examples.
method Combining non-linear dimensionality reduction and density estimation.
result Effective detection of adversarial examples by non-adaptive attackers.
The paper proves a new version of dimensional reduction in cohomological Donaldson-Thomas theory.
problem Proving a new version of dimensional reduction in cohomological Donaldson-Thomas theory.
method Using cohomological Donaldson-Thomas theory and loop stacks of 0-shifted symplectic stacks.
result Shows the BPS cohomology of loop stacks admits a description analogous to orbifold cohomology.
Modern techniques simplify complex high-dimensional data.
problem Complex, high-dimensional data.
method Unsupervised dimension reduction techniques.
result Simplified representation of high-dimensional data.
Fuzzy clustering reduces text data dimensions effectively.
problem Sparsity and high dimensionality in document-term frequency matrices.
method Fuzzy clustering applied as a new unsupervised feature transformation method.
result Fuzzy clustering outperforms PCA and SVD in reducing text data dimensions.
Unified model for reducing dimensions and clustering high-dimensional data.
problem High-dimensional data clustering and dimensionality reduction.
method Hierarchical mixtures of Gaussians (HMoGs) with closed-form likelihood and inference.
result Efficiently models hundreds of latent dimensions, improving clustering performance.