This work proposes a novel method for semi-supervised learning from partially labeled massive network-structured datasets, i.e., big data over networks. We model the underlying hypothesis, which relates data points to labels, as a graph signal, defined over some graph (network) structure intrinsic to the dataset. Following the key principle of supervised learning, i.e., similar inputs yield similar outputs, we require the graph signals induced by labels to have small total variation. Accordingly, we formulate the problem of learning the labels of data points as a non-smooth convex optimization problem which amounts to balancing between the empirical loss, i.e., the discrepancy with some partially available label information, and the smoothness quantified by the total variation of the learned graph signal. We solve this optimization problem by appealing to a recently proposed preconditioned variant of the popular primal-dual method by Pock and Chambolle, which results in a sparse label propagation algorithm. This learning algorithm allows for a highly scalable implementation as message passing over the underlying data graph. By applying concepts of compressed sensing to the learning problem, we are also able to provide a transparent sufficient condition on the underlying network structure such that accurate learning of the labels is possible. We also present an implementation of the message passing formulation allows for a highly scalable implementation in big data frameworks.

Unsupervised learning is the Holy Grail of Deep Learning. The goal of unsupervised learning is to create general systems that can be trained with little data. Today Deep Learning models are trained on large supervised datasets. Meaning that for each data, there is a corresponding label. In the case of the popular ImageNet dataset, there are 1M images labeled by humans.

In this paper, we presented a novel semi-supervised one-class classification algorithm which assumes that class is linearly separable from other elements. We proved theoretically that class is linearly separable if and only if it is maximal by probability within the sets with the same mean. Furthermore, we presented an algorithm for identifying such linearly separable class utilizing linear programming. We described three application cases including an assumption of linear separability, Gaussian distribution, and the case of linear separability in transformed space of kernel functions. Finally, we demonstrated the work of the proposed algorithm on the USPS dataset and analyzed the relationship of the performance of the algorithm and the size of the initially labeled sample.

Automatically assessing emotional valence in human speech has historically been a difficult task for machine learning algorithms. The subtle changes in the voice of the speaker that are indicative of positive or negative emotional states are often "overshadowed" by voice characteristics relating to emotional intensity or emotional activation. In this work we explore a representation learning approach that automatically derives discriminative representations of emotional speech. In particular, we investigate two machine learning strategies to improve classifier performance: (1) utilization of unlabeled data using a deep convolutional generative adversarial network (DCGAN), and (2) multitask learning. Within our extensive experiments we leverage a multitask annotated emotional corpus as well as a large unlabeled meeting corpus (around 100 hours). Our speaker-independent classification experiments show that in particular the use of unlabeled data in our investigations improves performance of the classifiers and both fully supervised baseline approaches are outperformed considerably. We improve the classification of emotional valence on a discrete 5-point scale to 43.88% and on a 3-point scale to 49.80%, which is competitive to state-of-the-art performance.

In Part I of this blog series, I described machine learning's history as well as its current prevailing ideas. My introduction was purposely general because my objective was to cement in the reader's mind exactly what machine learning is, and is not. Here in Part II, I dig in a bit deeper and differentiate between the various categories of machine learning. Recall from Part I that machine learning literally entails computers learning – either from data, or from their environment. In general, there are several ways in which computers learn (referred to here as categories).

Machines that can think for themselves - and perhaps turn on their creators as a result - have long been a fascination of science fiction. And creating robots that can learn without any input from humans is moving ever closer, thanks to the latest developments in artificial intelligence. One such project seeks to pit the wits of two AI algorithms against each other, with results that could one day lead to the emergence of such intelligent machines. Researchers have pitted AI algorithms against each other to create more realistic'imaginings' of the real world. Google's Generative Adversarial Network works by pitting two algorithms against each other, in an attempt to create convincing representations of the real world.

This resource is designed primarily for beginning data scientists or analysts who are interested in identifying and applying machine learning algorithms to address the problems of their interest. 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.

In a previous post I summarized the tasks and procedures available in SAS Viya Data Mining and Machine Learning. In this post, I'll dive into the unsupervised learning category which currently hosts several tasks: Kmeans, Kmodes, and Kprototypes Clustering, Outlier Detection, and a few variants of Principal Component Analysis. In unsupervised learning there are no known labels (outcomes), only attributes (inputs). Examples include clustering, association, and segmentation. Machine learning finds high density areas (in multidimensional space) that are more or less similar to each other, and identifies structures in the data that separate these areas.

We propose a general approach to modeling semi-supervised learning (SSL) algorithms. Specifically, we present a declarative language for modeling both traditional supervised classification tasks and many SSL heuristics, including both well-known heuristics such as co-training and novel domain-specific heuristics. In addition to representing individual SSL heuristics, we show that multiple heuristics can be automatically combined using Bayesian optimization methods. We experiment with two classes of tasks, link-based text classification and relation extraction. We show modest improvements on well-studied link-based classification benchmarks, and state-of-the-art results on relation-extraction tasks for two realistic domains.

You can do alternating training manually by literally following the algorithm, so that you have a Do loop whose body contains two calls to NetTrain, but that suffers from overhead at each alternation (this could be overcome with clever caching, but we haven't done that yet). An approximation of this is to build a single network and optimize the D and G losses simultaneously by using a negative learning rate for the generator. I have prototyped this, but only on a toy example. I encourage you to try how to do it, it didn't take us more than a few hours of playing around to make a simple GAN in which the data distribution is a gaussian, the discriminator is an MLP, and the generator is a single EmbeddingLayer (just a fixed set of samples that can be moved around by gradient updates).