Although many companies today possess massive amounts of data, the vast majority of that data is often unstructured and unlabeled. In fact, the amount of data that is appropriately labeled for a specific business need is typically quite small (possibly even zero), and acquiring new labels is usually a slow, expensive endeavor. As a result, algorithms that can extract features from unlabeled data to improve the performance of data-limited tasks are quite valuable. Most machine learning practitioners are first exposed to feature extraction techniques through unsupervised learning. In unsupervised learning, an algorithm attempts to discover the latent features that describe a data set's "structure" under certain (either explicit or implicit) assumptions.

The foundation of every machine learning project is data – the one thing you cannot do without. In this post, I will show how a simple semi-supervised learning method called pseudo-labeling that can increase the performance of your favorite machine learning models by utilizing unlabeled data. To train a machine learning model with supervised learning, the data has to be labeled. Does that mean that unlabeled data is useless for supervised tasks like classification and regression? Aside from using the extra data for analytic purposes, we can even use it to help train our model with semi-supervised learning – combining both unlabeled and labeled data for model training.

How do you find the underlying structure of a dataset? How do you summarize it and group it most usefully? How do you effectively represent data in a compressed format? These are the goals of unsupervised learning, which is called "unsupervised" because you start with unlabeled data (there's no Y). The two unsupervised learning tasks we will explore are clustering the data into groups by similarity and reducing dimensionality to compress the data while maintaining its structure and usefulness.

Similarity-based clustering and semi-supervised learning methods separate the data into clusters or classes according to the pairwise similarity between the data, and the pairwise similarity is crucial for their performance. In this paper, we propose a novel discriminative similarity learning framework which learns discriminative similarity for either data clustering or semi-supervised learning. The proposed framework learns classifier from each hypothetical labeling, and searches for the optimal labeling by minimizing the generalization error of the learned classifiers associated with the hypothetical labeling. Kernel classifier is employed in our framework. By generalization analysis via Rademacher complexity, the generalization error bound for the kernel classifier learned from hypothetical labeling is expressed as the sum of pairwise similarity between the data from different classes, parameterized by the weights of the kernel classifier. Such pairwise similarity serves as the discriminative similarity for the purpose of clustering and semi-supervised learning, and discriminative similarity with similar form can also be induced by the integrated squared error bound for kernel density classification. Based on the discriminative similarity induced by the kernel classifier, we propose new clustering and semi-supervised learning methods. 1 Y. Yang et al.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is "which algorithm should I use?" Even an experienced data scientist cannot tell which algorithm will perform the best before trying different algorithms. We are not advocating a one and done approach, but we do hope to provide some guidance on which algorithms to try first depending on some clear factors. The machine learning algorithm cheat sheet helps you to choose from a variety of machine learning algorithms to find the appropriate algorithm for your specific problems. This article walks you through the process of how to use the sheet.

With recent progress in graphics, it has become more tractable to train models on synthetic images, poten- tially avoiding the need for expensive annotations. How- ever, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions. To reduce this gap, we pro- pose Simulated Unsupervised (S U) learning, where the task is to learn a model to improve the realism of a simulator's output using unlabeled real data, while preserving the annotation information from the simula- tor. We develop a method for S U learning that uses an adversarial network similar to Generative Adversarial Networks (GANs), but with synthetic images as inputs instead of random vectors. We make several key modifi- cations to the standard GAN algorithm to preserve an- notations, avoid artifacts, and stabilize training: (i) a'self-regularization' term, (ii) a local adversarial loss, and (iii) updating the discriminator using a history of refined images.

The act of clustering, or spotting patterns in data, is not much different from spotting patterns in groups of people. Before jumping into action, we'll begin by taking an in-depth look at exactly what clustering entails. Clustering is an unsupervised machine learning task that automatically divides the data into clusters, or groups of similar items. It does this without having been told how the groups should look ahead of time. As we may not even know what we're looking for, clustering is used for knowledge discovery rather than prediction. It provides an insight into the natural groupings found within data.

The latent layer consists of 5 neurons, one of which is responsible for GI (efficiency against cancer cells) and the four others are discriminated with normal distribution. So, a regression term was added to the Encoder cost function. Furthermore, the Encoder was restricted to map the same fingerprint to the same latent vector, independently from input concentration by additional manifold cost. After training, it is possible to generate molecules from a desired distribution and use a GI-neuron as a tuner of output compounds. Results of this work are the following: the trained AAE model predicted compounds that are already proven to be anticancer agents and new untested compounds that should be validated with experiments on anticancer activity.

Disagreement-based approaches generate multiple classifiers and exploit the disagreement among them with unlabeled data to improve learning performance. Co-training is a representative paradigm of them, which trains two classifiers separately on two sufficient and redundant views; while for the applications where there is only one view, several successful variants of co-training with two different classifiers on single-view data instead of two views have been proposed. For these disagreement-based approaches, there are several important issues which still are unsolved, in this article we present theoretical analyses to address these issues, which provides a theoretical foundation of co-training and disagreement-based approaches. Keywords: machine learning, semi-supervised learning, disagreement-based learning, co-training, multi-view classification, combination 1. Introduction Learning from labeled training data is well-established in traditional machine learning, but labeling the data is time-consuming, sometimes may be very expensive since it requires human efforts. In many practical applications, unlabeled data can be obtained abundantly and cheaply.