Missing Value Imputation With Unsupervised Backpropagation

Many data mining and data analysis techniques operate on dense matrices or complete tables of data. Real-world data sets, however, often contain unknown values. Even many classification algorithms that are designed to operate with missing values still exhibit deteriorated accuracy. One approach to handling missing values is to fill in (impute) the missing values. In this paper, we present a technique for unsupervised learning called Unsupervised Backpropagation (UBP), which trains a multi-layer perceptron to fit to the manifold sampled by a set of observed point-vectors. We evaluate UBP with the task of imputing missing values in datasets, and show that UBP is able to predict missing values with significantly lower sum-squared error than other collaborative filtering and imputation techniques. We also demonstrate with 24 datasets and 9 supervised learning algorithms that classification accuracy is usually higher when randomly-withheld values are imputed using UBP, rather than with other methods

Recommending Learning Algorithms and Their Associated Hyperparameters

The success of machine learning on a given task dependson, among other things, which learning algorithm is selected and its associated hyperparameters. Selecting an appropriate learning algorithm and setting its hyperparameters for a given data set can be a challenging task, especially for users who are not experts in machine learning. Previous work has examined using meta-features to predict which learning algorithm and hyperparameters should be used. However, choosing a set of meta-features that are predictive of algorithm performance is difficult. Here, we propose to apply collaborative filtering techniques to learning algorithm and hyperparameter selection, and find that doing so avoids determining which meta-features to use and outperforms traditional meta-learning approaches in many cases.Comment: Short paper--2 pages, 2 table

A Continuous Space Generative Model

Generative models are a class of machine learning models capable of producing digital images with plausibly realistic properties. They are useful in such applications as visualizing designs, rendering game scenes, and improving images at higher magnifications. Unfortunately, existing generative models generate only images with a discrete predetermined resolution. This paper presents the Continuous Space Generative Model (CSGM), a novel generative model capable of generating images as a continuous function, rather than as a discrete set of pixel values. Like generative adversarial networks, CSGM trains by alternating between generative and discriminative steps. But unlike generative adversarial networks, CSGM uses only one model for both steps, such that learning can transfer between both operations. Also, the continuous images that CSGM generates may be sampled at arbitrary resolutions, opening the way for new possibilities with generative models. This paper presents results obtained by training on the MNIST dataset of handwritten digits to validate the method, and it elaborates on the potential applications for continuous generative models

Information technologies: science, engineering, technology, education, health. Part 4

Подано тези доповідей науково-практичної конференції MicroCAD-2017 за теоретичними та практичними результатами наукових досліджень і розробок, які виконані викладачами вищої школи, науковими співробітниками, аспірантами, студентами, фахівцями різних організацій і підприємств. Для викладачів, наукових працівників, аспірантів, студентів, фахівців. Тези доповідей відтворені з авторських оригіналів

Information technologies: science, engineering, technology, education, health. Part 4

Подано тези доповідей науково-практичної конференції MicroCAD-2017 за теоретичними та практичними результатами наукових досліджень і розробок, які виконані викладачами вищої школи, науковими співробітниками, аспірантами, студентами, фахівцями різних організацій і підприємств. Для викладачів, наукових працівників, аспірантів, студентів, фахівців. Тези доповідей відтворені з авторських оригіналів