КЛАСИФІКАЦІЯ МНОЖИН МЕТОДОМ ЛІНІЙНОГО ВІДОКРЕМЛЕННЯ ЇХ ОПУКЛИХ ОБОЛОНОК

У статті представлений метод лінійного відокремлення опуклих оболонок (ЛВОО) для класифікації двох множин в евклідовому просторі Rn . Наводяться приклади для порівнян- ня результатів класифікації методом ЛВОО, а також дискри- мінантним аналізом, наївним байєсівським класифікатором та методом опорних векторів

Класифікація множин методом лінійного відокремлення їх опуклих оболонок

У статті представлений метод лінійного відокремлення опуклих оболонок (ЛВОО) для класифікації двох множин в евклідовому просторі Rⁿ . Наводяться приклади для порівняння результатів класифікації методом ЛВОО, а також дискримінантним аналізом, наївним байєсівським класифікатором та методом опорних векторів.The method of convex hulls linear separation for classification of two sets in Euclidian space in Rⁿ is proposed. Examples for comparing the results of using the method of convex hulls linear separation and discriminant analysis, naive Bayes classifier and SVM are described in the manuscript

Fusing Vantage Point Trees and Linear Discriminants for Fast Feature Classification

This paper describes a classification strategy that can be regarded as amore general form of nearest-neighbor classification. It fuses the concepts ofnearestneighbor,linear discriminantandVantage-Pointtrees, yielding an efficient indexingdata structure and classification algorithm. In the learning phase, we define a set ofdisjoint subspaces of reduced complexity that can be separated by linear discrimi-nants, ending up with an ensemble of simple (weak) classifiers that work locally. Inclassification, the closest centroids to the query determine the set of classifiers con-sidered, which responses are weighted. The algorithm was experimentally validatedin datasets widely used in the field, attaining error rates that are favorably compara-ble to the state-of-the-art classification techniques. Lastly, the proposed solution hasa set of interesting properties for a broad range of applications: 1) it is determinis-tic; 2) it classifies in time approximately logarithmic with respect to the size of thelearning set, being far more efficient than nearest neighbor classification in terms ofcomputational cost; and 3) it keeps the generalization ability of simple models.info:eu-repo/semantics/publishedVersio

LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures

Joint embedding (JE) architectures have emerged as a promising avenue for acquiring transferable data representations. A key obstacle to using JE methods, however, is the inherent challenge of evaluating learned representations without access to a downstream task, and an annotated dataset. Without efficient and reliable evaluation, it is difficult to iterate on architectural and training choices for JE methods. In this paper, we introduce LiDAR (Linear Discriminant Analysis Rank), a metric designed to measure the quality of representations within JE architectures. Our metric addresses several shortcomings of recent approaches based on feature covariance rank by discriminating between informative and uninformative features. In essence, LiDAR quantifies the rank of the Linear Discriminant Analysis (LDA) matrix associated with the surrogate SSL task -- a measure that intuitively captures the information content as it pertains to solving the SSL task. We empirically demonstrate that LiDAR significantly surpasses naive rank based approaches in its predictive power of optimal hyperparameters. Our proposed criterion presents a more robust and intuitive means of assessing the quality of representations within JE architectures, which we hope facilitates broader adoption of these powerful techniques in various domains.Comment: Technical repor

Feature extraction and segmentation of hyperspectral images

This work proposes an approach for hyperspectral images segmentation without direct application of common clustering methods on the hyperspectral data. The proposed approach reduces the spectral dimension of the image, through principal component analysis, and its spatial dimension, through wavelet transform, in order to apply the clustering algorithm on a lower resolution version of the data and then train a classifier to label the high resolution image

Visual exploration of topics in multimedia news corpora

As news contents grow daily, the demand for tools to help users make sense of large document corpus will continuously be on the increase. Such tools will particularly be useful for journalist and ordinary users who intend to explore large collection of news documents for various analytical tasks. When users attempt to explore documents, they are usually in search for a particular topic of interest, or to compare various topics for similarity, or to see when in time a particular topic was discussed or to explore the distribution of a topic over time or to see how frequent a particular topic was discussed in the corpus or in general to test a particular hypothesis. Existing tools fall short in providing effective and suitable interaction mechanism to enable users answer these questions in a single application framework. In this paper we presented a framework that gives users the opportunity to easily answer questions relating to their exploratory tasks. We developed new visual elements and augment them with existing interfaces to provide users with ample options and flexibility to explore multimedia news corpus from different angles depending on their analytic tasks. Our method uses machine learning for topic extraction, clustering and word cloud generation. Our approach effectively combines both overview + detail and focus + context schemes to enrich users experience with exploring large collection of multimedia news documents. Our framework ensures synchronization of the various visual interfaces to provide immediate feedback on user's interactions. To demonstrate the effectiveness of our approach, we presented some realistic use cases from the perspective of a news analyst. And based on our observations, we identified some possible directions for future studies