Angular triangle distance for ordinal metric learning

Deep metric learning (DML) aims to automatically construct task-specific distances or similarities of data, resulting in a low-dimensional representation. Several significant metric-learning methods have been proposed. Nonetheless, no approach guarantees the preservation of the ordinal nature of the original data in a low-dimensional space. Ordinal data are ubiquitous in real-world problems, such as the severity of symptoms in biomedical cases, production quality in manufacturing, rating level in businesses, and aging level in face recognition. This study proposes a novel angular triangle distance (ATD) and ordinal triplet network (OTD) to obtain an accurate and meaningful embedding space representation for ordinal data. The ATD projects the ordinal relation of data in the angular space, whereas the OTD learns its ordinal projection. We also demonstrated that our new distance measure satisfies the distance metric properties mathematically. The proposed method was assessed using real-world data with an ordinal nature, such as biomedical, facial, and hand-gestured images. Extensive experiments have been conducted, and the results show that our proposed method not only semantically preserves the ordinal nature but is also more accurate than existing DML models. Moreover, we also demonstrate that our proposed method outperforms the state-of-the-art ordinal metric learning method

Comparative user feedback rating

This disclosure describes comparative user feedback for products and services. Users are presented with choices of products and/or services and requested to make an ordered selection of their preferred choice(s). A user’s spatial ordering of choices on a slider provides information about the degree of user preferences. The comparative user feedback data are analyzed and used to train a machine learning (ML) model using ordinal regression. Absolute training data are provided to the machine learning model as seed training data. Ordinal ML regression is used to generate a comparison metric based on the comparison data, and is utilized for ranking user preferences and providing recommendations. The comparative feedback is integrated into the user’s profile that is utilized to generate user feedback candidates and provide recommendations

Mammography Image BI-RADS Classification Using OHPLall

Medical image analysis and classification, using machine learning, particularly Convolutional Neural Networks, have demonstrated a great deal of success. Research into mammography image classification tended to focus on either binary outcome (malignancy or benign) or nominal (unordered) classification for multiclass labels [1]. The industry standard metric for radiologist’s classification of mammography images is a rating scale called BI-RADS (Breast Imaging Reporting and Data System), where values 1 through 5 are a distinct progression of assessment that are intended to denote higher risk of a malignancy, based on the characteristics of anomalies within an image [1][2][3]. The development of a classifier that predicts BI-RADS 1-5, would provide radiologists with an objective second opinion on image anomalies. In this paper, we applied a novel Deep Learning method called OHPLall (Ordinal Hyperplane Loss - all centroids), which was specifically designed for data with ordinal classes, to the predictions of BI-RADS scales on mammography images. Our experimental study demonstrated promising results generated by OHPLall and great potential of using OHPLall models as a supplemental diagnostic tool

A Learned Index for Exact Similarity Search in Metric Spaces

Indexing is an effective way to support efficient query processing in large databases. Recently the concept of learned index has been explored actively to replace or supplement traditional index structures with machine learning models to reduce storage and search costs. However, accurate and efficient similarity query processing in high-dimensional metric spaces remains to be an open challenge. In this paper, a novel indexing approach called LIMS is proposed to use data clustering and pivot-based data transformation techniques to build learned indexes for efficient similarity query processing in metric spaces. The underlying data is partitioned into clusters such that each cluster follows a relatively uniform data distribution. Data redistribution is achieved by utilizing a small number of pivots for each cluster. Similar data are mapped into compact regions and the mapped values are totally ordinal. Machine learning models are developed to approximate the position of each data record on the disk. Efficient algorithms are designed for processing range queries and nearest neighbor queries based on LIMS, and for index maintenance with dynamic updates. Extensive experiments on real-world and synthetic datasets demonstrate the superiority of LIMS compared with traditional indexes and state-of-the-art learned indexes.Comment: 14 pages, 14 figures, submitted to Transactions on Knowledge and Data Engineerin