arXiv research

This work improves sample efficiency in meta-learning for nonlinear tasks.

ProSub uses angles in feature space to classify data as in- or out-of-distribution.

New method reduces high-dimensional data to key features.

Text classification has become indispensable due to the rapid increase of text in digital form. Over the past three decades, efforts have been made to approach this task using various learning algorithms and statistical models based on bag-of-words (BOW) features. Despite its simple implementation, BOW features lack se…

In this work, we propose a subspace-based algorithm for DOA estimation which iteratively reduces the disturbance factors of the estimated data covariance matrix and incorporates prior knowledge which is gradually obtained on line. An analysis of the MSE of the reshaped data covariance matrix is carried out along with c…

Spectral Method is a commonly used scheme to cluster data points lying close to Union of Subspaces by first constructing a Random Geometry Graph, called Subspace Clustering. This paper establishes a theory to analyze this method. Based on this theory, we demonstrate the efficiency of Subspace Clustering in fairly broad…

Unified multi-view learning framework using OPLS with regularization and deep extensions.

Deep ensembles have been empirically shown to be a promising approach for improving accuracy, uncertainty and out-of-distribution robustness of deep learning models. While deep ensembles were theoretically motivated by the bootstrap, non-bootstrap ensembles trained with just random initialization also perform well in p…

New Krylov subspace methods speed up mixed-effects models with crossed random effects.

A new method for anomaly detection using random subspaces and Gaussian mixture models.

ALPCAHUS clusters data from multiple subspaces with varying noise.

Rare data in a large-scale database are called outliers that reveal significant information in the real world. The subspace-based outlier detection is regarded as a feasible approach in very high dimensional space. However, the outliers found in subspaces are only part of the true outliers in high dimensional space, in…

A new method tackles bilevel optimization using Lanczos process for efficient hyper-gradient computation.

LOFT separates subspace rotation and transformation for orthogonal fine-tuning.

PCA adapted for curved spaces improves data analysis.

Random projections are able to perform dimension reduction efficiently for datasets with nonlinear low-dimensional structures. One well-known example is that random matrices embed sparse vectors into a low-dimensional subspace nearly isometrically, known as the restricted isometric property in compressed sensing. In th…

New CRB derived for curved models using extrinsic geometry.

Error-robust multi-view clustering tackles noisy data across multiple sources.

This paper studies the problem of data-adaptive representations for big, distributed data. It is assumed that a number of geographically-distributed, interconnected sites have massive local data and they are interested in collaboratively learning a low-dimensional geometric structure underlying these data. In contrast …

Subspace clustering (SC) refers to the problem of clustering high-dimensional data into a union of low-dimensional subspaces. Based on spectral clustering, state-of-the-art approaches solve SC problem within a two-stage framework. In the first stage, data representation techniques are applied to draw an affinity matrix…

Hyperspectral remote sensing images (HSIs) usually have high spectral resolution and low spatial resolution. Conversely, multispectral images (MSIs) usually have low spectral and high spatial resolutions. The problem of inferring images which combine the high spectral and high spatial resolutions of HSIs and MSIs, resp…

This paper discusses a new type of discriminant analysis based on the orthogonal projection of data onto a generalized difference subspace (GDS). In our previous work, we have demonstrated that GDS projection works as the quasi-orthogonalization of class subspaces, which is an effective feature extraction for subspace …

Improved Bandit PCA with optimal regret bound.

Physics-based framework improves building energy forecasting.

The main goal of this paper is to study the geometric structures associated with the representation of tensors in subspace based formats. To do this we use a property of the so-called minimal subspaces which allows us to describe the tensor representation by means of a rooted tree. By using the tree structure and the d…

A new method compresses NLP networks by using multiple subspaces instead of a single one.

Dual-stage sEMG classification improves gesture recognition accuracy.

A novel method for classification with rejection using ensemble of cost-sensitive classifiers.

The number of possible methods of generalizing binary classification to multi-class classification increases exponentially with the number of class labels. Often, the best method of doing so will be highly problem dependent. Here we present classification software in which the partitioning of multi-class classification…

Sequence classification is an important data mining task in many real world applications. Over the past few decades, many sequence classification methods have been proposed from different aspects. In particular, the pattern-based method is one of the most important and widely studied sequence classification methods in …

New NHCAs improve multi-category classification efficiency.

Classification outperforms regression in portfolio construction, yielding higher Sharpe ratios.

C-HMCNN(h) improves HMC classification by leveraging class hierarchy.

Advances few-shot classification by treating it as supervised learning and proposing new training techniques.

We study realizations of Lie algebras by vector fields. A correspondence between classification of transitive local realizations and classification of subalgebras is generalized to the case of regular local realizations. A reasonable classification problem for general realizations is rigorously formulated and an algori…

New approach improves classification guarantees by focusing on direction rather than regression risk.

Study selective classification with halfspaces, achieving error bounds under Gaussian distributions.

A new network-based high-level data classification method using betweenness centrality.

This thesis evaluates text-based vs audio-based classification of mental health interviews.

Study on error probability for classification of heavy-tailed renewal processes.

Directly compute classification by learning features with class scores.

Conventional techniques for supervised classification constrain the classification rules considered and use surrogate losses for classification 0-1 loss. Favored families of classification rules are those that enjoy parametric representations suitable for surrogate loss minimization, and low complexity properties suita…

Text classification on drug SMILES strings yields competitive drug type classification results.

Paper proposes fully Bayesian approach for RVM classification, improving accuracy especially in imbalanced data.

Scientists have used many different classification methods to solve the problem of music classification. But the efficiency of each classification is different. In this paper, we propose two compared methods on the task of music style classification. More specifically, feature extraction for representing timbral textur…

This work bounds classification error in machine learning for low Bayes error conditions.

Proposes an angle-based framework for multicategory cost-sensitive classification.

Optimal fuzzy classification aggregation functions are weighted means.