Compositional data are non-negative data collected in a rectangular matrix with a constant row sum. Due to the non-negativity the focus is on conditional proportions that add up to 1 for each row. A row of conditional proportions is called an observed budget. Latent budget analysis (LBA) assumes a mixture of latent budgets that explains the observed budgets. LBA is usually fitted to a contingency table, where the rows are levels of one or more explanatory variables and the columns the levels of a response variable. In prospective studies, there is only knowledge about the explanatory variables of individuals and interest goes out to predicting the response variable. Thus, a form of LBA is needed that has the functionality of prediction. Previous studies proposed a constrained neural network (NN) extension of LBA that was hampered by an unsatisfying prediction ability. Here we propose LBA-NN, a feed forward NN model that yields a similar interpretation to LBA but equips LBA with a better ability of prediction. A stable and plausible interpretation of LBA-NN is obtained through the use of importance plots and table, that show the relative importance of all explanatory variables on the response variable. An LBA-NN-K- means approach that applies K-means clustering on the importance table is used to produce K clusters that are comparable to K latent budgets in LBA. Here we provide different experiments where LBA-NN is implemented and compared with LBA. In our analysis, LBA-NN outperforms LBA in prediction in terms of accuracy, specificity, recall and mean square error. We provide open-source software at GitHub.

K-means clustering is one of the simplest and popular unsupervised machine learning algorithms. Unsupervised Learning is a machine learning technique in which, there are no labels for the training data. A machine learning algorithm tries to learn the underlying patterns or distributions that govern the data. Clustering is one of the most common exploratory data analysis techniques used to get an intuition about the structure of the data. It can be defined as the task of identifying subgroups in the data such that data points in the same subgroup (cluster) are very similar while data points in different clusters are very different.

Non-negative matrix factorization (NMF) has become a popular method for representing meaningful data by extracting a non-negative basis feature from an observed non-negative data matrix. Some of the unique features of this method in identifying hidden data put this method amongst the powerful methods in the machine learning area. The NMF is a known non-convex optimization problem and the initial point has a significant effect on finding an efficient local solution. In this paper, we investigate the most popular initialization procedures proposed for NMF so far. We describe each method and present some of their advantages and disadvantages. Finally, some numerical results to illustrate the performance of each algorithm are presented.

A novel procedure to perform fuzzy clustering of multivariate time series generated from different dependence models is proposed. Different amounts of dissimilarity between the generating models or changes on the dynamic behaviours over time are some arguments justifying a fuzzy approach, where each series is associated to all the clusters with specific membership levels. Our procedure considers quantile-based cross-spectral features and consists of three stages: (i) each element is characterized by a vector of proper estimates of the quantile cross-spectral densities, (ii) principal component analysis is carried out to capture the main differences reducing the effects of the noise, and (iii) the squared Euclidean distance between the first retained principal components is used to perform clustering through the standard fuzzy C-means and fuzzy C-medoids algorithms. The performance of the proposed approach is evaluated in a broad simulation study where several types of generating processes are considered, including linear, nonlinear and dynamic conditional correlation models. Assessment is done in two different ways: by directly measuring the quality of the resulting fuzzy partition and by taking into account the ability of the technique to determine the overlapping nature of series located equidistant from well-defined clusters. The procedure is compared with the few alternatives suggested in the literature, substantially outperforming all of them whatever the underlying process and the evaluation scheme. Two specific applications involving air quality and financial databases illustrate the usefulness of our approach.

Event-based cameras are bio-inspired sensors that mimic the working of the human eye (Gallego et al. [2020]). While frame-based cameras capture images at a definite frame rate which is determined by an external clock, each pixel in event-based cameras memorizes the log intensity each time an event is sent and simultaneously monitors for a sufficient change in magnitude from this memorized threshold value (Gallego et al. [2020]). The event is recorded by the camera and is transmitted by the sensor in the form of its location {x, y}, its time of occurrence (timestamp) t and its polarity p (taking a binary value 1 or 1, representing whether the pixel is brighter or darker) (Chen et al. [2020]). The working of an event-based camera is shown in Figure 1. The sensors used in event-based cameras are data-driven, for their output depends on the amount of motion or brightness change in the scene (Gallego et al. [2020]). Higher is the motion, higher is the number of events generated. The events are recorded in microsecond resolution and are transmitted in sub-millisecond latency, making these sensors react quickly to visual stimuli (Gallego et al. [2020]). Thus, while frame-based cameras capture the absolute brightness of a scene, event-based cameras capture the per-pixel brightness asynchronously, making traditional computer vision algorithms inapplicable to be implemented for processing the event data. Detection of moving objects is an important task in automation, where a computer differentiates in between a moving object and a stationary one.

In this article, I'll explain supervised vs unsupervised learning. The tutorial will start by discussing some foundational concepts and then it will explain supervised and unsupervised learning separately, in more detail. If you need something specific, just click on the link. The following links will take you to specific sections of the article. Having said that, if you're confused about supervised vs unsupervised learning, you'll probably want to read the whole article from start to finish. If you're somewhat new to machine learning, you've probably heard the terms "supervised" and "unsupervised" learning.

Hierarchical Clustering is a part of unsupervised Machine Learning. Hierarchical clustering is also known as Hierarchical Cluster Analysis (HCA) is unsupervised Machine Learning. It groups unlabeled data sets into groups also Known as clusters. They look quite similar to K-means Clustering but it's different from it as here we don't decide the number of clusters as we do it in K-means Clustering. As here we don't face the challenges that we face in K-mean clustering due to predetermined clusters.

This paper proposes a multi-label classification algorithm capable of continual learning by applying an Adaptive Resonance Theory (ART)-based clustering algorithm and the Bayesian approach for label probability computation. The ART-based clustering algorithm adaptively and continually generates prototype nodes corresponding to given data, and the generated nodes are used as classifiers. The label probability computation independently counts the number of label appearances for each class and calculates the Bayesian probabilities. Thus, the label probability computation can cope with an increase in the number of labels. Experimental results with synthetic and real-world multi-label datasets show that the proposed algorithm has competitive classification performance to other well-known algorithms while realizing continual learning.

We revisit the problem of fair clustering, first introduced by Chierichetti et al., that requires each protected attribute to have approximately equal representation in every cluster; i.e., a balance property. Existing solutions to fair clustering are either not scalable or do not achieve an optimal trade-off between clustering objective and fairness. In this paper, we propose a new notion of fairness, which we call $tau$-fair fairness, that strictly generalizes the balance property and enables a fine-grained efficiency vs. fairness trade-off. Furthermore, we show that simple greedy round-robin based algorithms achieve this trade-off efficiently. Under a more general setting of multi-valued protected attributes, we rigorously analyze the theoretical properties of the our algorithms. Our experimental results suggest that the proposed solution outperforms all the state-of-the-art algorithms and works exceptionally well even for a large number of clusters.

Like most undergraduates right out of college with little to no first-hand experience working on industry ML projects and loads of ML/python certifications, I joined the Business Intelligence team at Samsung. There were 3 new hires in the team and there was only 1 Data Scientist (DS) position available, the other 2 were Data Engineering. With the 3 of us riding the ML wave, we all sought the Data Scientist position. During the first meeting with our manager, you can imagine the amount of malarkey all the candidates spat out to get the position. We were given a 3-week trial period during which each of us had a Data Engineering pipeline to build and perform an Exploratory Data Analysis on a given dataset.