Search results for "HM"
showing 10 items of 10594 documents
Scalable Hierarchical Clustering: Twister Tries with a Posteriori Trie Elimination
2015
Exact methods for Agglomerative Hierarchical Clustering (AHC) with average linkage do not scale well when the number of items to be clustered is large. The best known algorithms are characterized by quadratic complexity. This is a generally accepted fact and cannot be improved without using specifics of certain metric spaces. Twister tries is an algorithm that produces a dendrogram (i.e., Outcome of a hierarchical clustering) which resembles the one produced by AHC, while only needing linear space and time. However, twister tries are sensitive to rare, but still possible, hash evaluations. These might have a disastrous effect on the final outcome. We propose the use of a metaheuristic algor…
Can back-projection fully resolve polarity indeterminacy of independent component analysis in study of event-related potential?
2011
a b s t r a c t In the study of event-related potentials (ERPs) using independent component analysis (ICA), it is a traditional way to project the extracted ERP component back to electrodes for correcting its scaling (magnitude and polarity) indeterminacy. However, ICA tends to be locally optimized in practice, and then, the back-projection of a component estimated by the ICA can possibly not fully correct its polarity at every electrode. We demonstrate this phenomenon from the view of the theoretical analysis and numerical simulations and suggest checking and modifying the abnormal polarity of the projected component in the electrode field before further analysis. Moreover, when several co…
Selection of the Proper Revenue and Pricing Model for SaaS
2014
Recent research on software revenue and pricing models has revealed important ways in which firms can benefit from software renting. However, it is still unclear how SaaS providers select a proper revenue and pricing model to make their services attractive for customers. Based on 32 interviews with software professionals from four case firms, this study reveals how different factors impacted on the selection of a revenue and pricing model. It can be concluded that customers’ needs were the main driving force to the selection of the most appropriate pricing and revenue model in the market. peerReviewed
Social Collaborative Viewpoint Regression with Explainable Recommendations
2017
A recommendation is called explainable if it not only predicts a numerical rating for an item, but also generates explanations for users' preferences. Most existing methods for explainable recommendation apply topic models to analyze user reviews to provide descriptions along with the recommendations they produce. So far, such methods have neglected user opinions and influences from social relations as a source of information for recommendations, even though these are known to improve the rating prediction. In this paper, we propose a latent variable model, called social collaborative viewpoint regression (sCVR), for predicting item ratings based on user opinions and social relations. To th…
Listwise Collaborative Filtering
2015
Recently, ranking-oriented collaborative filtering (CF) algorithms have achieved great success in recommender systems. They obtained state-of-the-art performances by estimating a preference ranking of items for each user rather than estimating the absolute ratings on unrated items (as conventional rating-oriented CF algorithms do). In this paper, we propose a new ranking-oriented CF algorithm, called ListCF. Following the memory-based CF framework, ListCF directly predicts a total order of items for each user based on similar users' probability distributions over permutations of the items, and thus differs from previous ranking-oriented memory-based CF algorithms that focus on predicting th…
Simplicity and the art of something more: A cognitive-semiotic approach to simplicity and complexity in human-technology interaction and design exper…
2018
In human–technology interaction, the balance between simplicity and complexity has been much discussed. Emphasis is placed on the value of simplicity when designing for usability. Often simplicity is interpreted as reductionism, which compromises both the affective nature of the design and usability itself. This paper takes a cognitive–semiotic approach toward understanding the dynamics between the utilitarian benefits of simplicity in design and the art of something more: considerate complexity. The cognitive–semiotic approach to human–technology design experience is a vehicle for explaining the relationship between simplicity and complexity, and this relationship’s multisensory character …
Feature Extractors for Describing Vehicle Routing Problem Instances
2016
The vehicle routing problem comes in varied forms. In addition to usual variants with diverse constraints and specialized objectives, the problem instances themselves – even from a single shared source - can be distinctly different. Heuristic, metaheuristic, and hybrid algorithms that are typically used to solve these problems are sensitive to this variation and can exhibit erratic performance when applied on new, previously unseen instances. To mitigate this, and to improve their applicability, algorithm developers often choose to expose parameters that allow customization of the algorithm behavior. Unfortunately, finding a good set of values for these parameters can be a tedious task that…
Recommending Serendipitous Items using Transfer Learning
2018
Most recommender algorithms are designed to suggest relevant items, but suggesting these items does not always result in user satisfaction. Therefore, the efforts in recommender systems recently shifted towards serendipity, but generating serendipitous recommendations is difficult due to the lack of training data. To the best of our knowledge, there are many large datasets containing relevance scores (relevance oriented) and only one publicly available dataset containing a relatively small number of serendipity scores (serendipity oriented). This limits the learning capabilities of serendipity oriented algorithms. Therefore, in the absence of any known deep learning algorithms for recommend…
Scalable implementation of dependence clustering in Apache Spark
2017
This article proposes a scalable version of the Dependence Clustering algorithm which belongs to the class of spectral clustering methods. The method is implemented in Apache Spark using GraphX API primitives. Moreover, a fast approximate diffusion procedure that enables algorithms of spectral clustering type in Spark environment is introduced. In addition, the proposed algorithm is benchmarked against Spectral clustering. Results of applying the method to real-life data allow concluding that the implementation scales well, yet demonstrating good performance for densely connected graphs. peerReviewed
Higher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithm
2019
Tensor decomposition is a powerful tool for analyzing multiway data. Nowadays, with the fast development of multisensor technology, more and more data appear in higherorder (order > 4) and nonnegative form. However, the decomposition of higher-order nonnegative tensor suffers from poor convergence and low speed. In this study, we propose a new nonnegative CANDECOM/PARAFAC (NCP) model using proximal algorithm. The block principal pivoting method in alternating nonnegative least squares (ANLS) framework is employed to minimize the objective function. Our method can guarantee the convergence and accelerate the computation. The results of experiments on both synthetic and real data demonstrate …