A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Proposes MR-SNE for multimodal data visualization.
Improves visualization of high-dimensional data by correcting misleading artifacts in neighbor embedding methods.
Unified view of SOMs and SNE from a common framework.
Study of -neighbors in Riemannian manifolds, proving infinite set of distances.
Neighbor embeddings balance attraction and repulsion to visualize data.
DSNE visualizes data velocity in lower dimensions.
Since its introduction, unsupervised representation learning has attracted a lot of attention from the research community, as it is demonstrated to be highly effective and easy-to-apply in tasks such as dimension reduction, clustering, visualization, information retrieval, and semi-supervised learning. In this work, we…
Revised ct-SNE improves t-SNE for visualizing data beyond labels.
Visualizes classification results with class maps.
UMAP's true loss function differs from what was previously thought, focusing on nearest neighbor graph similarities.
Visualizes deep neural networks for speech recognition using learned topographic filter maps.
We consider the problem of naming objects in complex, natural scenes containing widely varying object appearance and subtly different names. Informed by cognitive research, we propose an approach based on sharing context based object hypotheses between visual and lexical spaces. To this end, we present the Visual Seman…
Python package reduces hubness in high-dimensional data.
In the advent of big data era, interactive visualization of large data sets consisting of M*10^5+ high-dimensional feature vectors of length N (N ~ 10^3+), is an indispensable tool for data exploratory analysis. The state-of-the-art data embedding (DE) methods of N-D data into 2-D (3-D) visually perceptible space (e.g.…
Modern datasets and models are notoriously difficult to explore and analyze due to their inherent high dimensionality and massive numbers of samples. Existing visualization methods which employ dimensionality reduction to two or three dimensions are often inefficient and/or ineffective for these datasets. This paper in…
New methods explain NE embeddings by identifying key variables.
Centroid-Encoder reduces high-dimensional data for better visualization.
Recently manifold learning algorithm for dimensionality reduction attracts more and more interests, and various linear and nonlinear, global and local algorithms are proposed. The key step of manifold learning algorithm is the neighboring region selection. However, so far for the references we know, few of which propos…
Paper studies t-SNE convergence with generalized kernels.
FEALM learns features for better nonlinear DR of hidden patterns.
We introduce several techniques for sampling and visualizing the latent spaces of generative models. Replacing linear interpolation with spherical linear interpolation prevents diverging from a model's prior distribution and produces sharper samples. J-Diagrams and MINE grids are introduced as visualizations of manifol…
A commonly used evaluation metric for text-to-image synthesis is the Inception score (IS) \cite{inceptionscore}, which has been shown to be a quality metric that correlates well with human judgment. However, IS does not reveal properties of the generated images indicating the ability of a text-to-image synthesis method…
Theoretical analysis of t-SNE for visualizing clustered data.
SPINEX improves time series forecasting with explainable neighbors.
This paper finds a linear relationship between t-SNE perplexity and data set size.
ARS visualization improves t-SNE dynamics with tunable attraction and repulsion.
New approach learns graph representations by contrasting first-order neighbors and graph diffusion views.
SDSPCAAN combines supervised and local data structures for better dimensionality reduction.
The optimization of electric machines at multiple operating points is crucial for applications that require frequent changes on speeds and loads, such as the electric vehicles, to strive for the machine optimal performance across the entire driving cycle. However, the number of objectives that would need to be optimize…
This paper presents a novel application of a clustering algorithm developed for constructing a phylogenetic network to the correlation matrix for 126 stocks listed on the Shanghai A Stock Market. We show that by visualizing the correlation matrix using a Neighbor-Net network and using the circular ordering produced dur…
Paper tackles zero-shot activity recognition using video features and text embeddings.
New method makes quality metrics scale-invariant for high-dimensional data.
The output scores of a neural network classifier are converted to probabilities via normalizing over the scores of all competing categories. Computing this partition function, , is then linear in the number of categories, which is problematic as real-world problem sets continue to grow in categorical types, such as …
Interactive tool for better understanding t-SNE projections.
The t-distributed Stochastic Neighbor Embedding (t-SNE) is a powerful and popular method for visualizing high-dimensional data. It minimizes the Kullback-Leibler (KL) divergence between the original and embedded data distributions. In this work, we propose extending this method to other f-divergences. We analytically a…
Retrieving the most similar objects in a large-scale database for a given query is a fundamental building block in many application domains, ranging from web searches, visual, cross media, and document retrievals. State-of-the-art approaches have mainly focused on capturing the underlying geometry of the data manifolds…
This work proves the continuum limit of t-SNE for data visualization.
MIK improves t-SNE's local structure preservation in biological sequence data.
Our goal is to extract meaningful transformations from raw images, such as varying the thickness of lines in handwriting or the lighting in a portrait. We propose an unsupervised approach to learn such transformations by attempting to reconstruct an image from a linear combination of transformations of its nearest neig…
Existing popular methods for semi-supervised learning with Graph Neural Networks (such as the Graph Convolutional Network) provably cannot learn a general class of neighborhood mixing relationships. To address this weakness, we propose a new model, MixHop, that can learn these relationships, including difference operat…
In this paper, a frequency coefficient based on the Sen-Shorrocks-Thon (SST) poverty index notion is proposed. The clustering SST index can be used as the method for determination of the connection between similar neighbor sub-clusters. Consequently, connections can reveal existence of natural homogeneous. Through esti…
JORC-UMAP improves UMAP by incorporating geometric and topological priors.
New machine learning method classifies companies effectively.
t-Distributed Stochastic Neighbor Embedding (t-SNE) is one of the most widely used dimensionality reduction methods for data visualization, but it has a perplexity hyperparameter that requires manual selection. In practice, proper tuning of t-SNE perplexity requires users to understand the inner working of the method a…
t-SNE fails to reveal clusters even in well-clusterable data.
Technical analysis is used to discover investment opportunities. To test this hypothesis we propose an hybrid system using machine learning techniques together with genetic algorithms. Using technical analysis there are more ways to represent a currency exchange time series than the ones it is possible to test computat…
LNPE enhances local connections in embeddings using extended neighbor propagation.
Graph representations have increasingly grown in popularity during the last years. Existing representation learning approaches explicitly encode network structure. Despite their good performance in downstream processes (e.g., node classification, link prediction), there is still room for improvement in different aspect…