Feature-Based Dynamic Pricing

2020 ◽  
Vol 66 (11) ◽  
pp. 4921-4943
Author(s):  
Maxime C. Cohen ◽  
Ilan Lobel ◽  
Renato Paes Leme
Keyword(s):  
Dynamic Pricing ◽  
Feature Space ◽  
Differentiated Products ◽  
Loan Pricing ◽  
Worst Case ◽  
Customer Base ◽  
Online Marketplaces ◽  
Uncertainty Sets ◽  
Posted Prices ◽  
Feature Based

We consider the problem faced by a firm that receives highly differentiated products in an online fashion. The firm needs to price these products to sell them to its customer base. Products are described by vectors of features and the market value of each product is linear in the values of the features. The firm does not initially know the values of the different features, but can learn the values of the features based on whether products were sold at the posted prices in the past. This model is motivated by applications such as online marketplaces, online flash sales, and loan pricing. We first consider a multidimensional version of binary search over polyhedral sets and show that it has a worst-case regret which is exponential in the dimension of the feature space. We then propose a modification of the prior algorithm where uncertainty sets are replaced by their Löwner-John ellipsoids. We show that this algorithm has a worst-case regret which is quadratic in the dimension of the feature space and logarithmic in the time horizon. We also show how to adapt our algorithm to the case where valuations are noisy. Finally, we present computational experiments to illustrate the performance of our algorithm. This paper was accepted by Yinyu Ye, optimization.

scholarly journals EMCM: A Novel Binary Edge-Feature-Based Maximum Clique Framework for Multispectral Image Matching

2019 ◽  
Vol 11 (24) ◽  
pp. 3026
Author(s):  
Bin Fang ◽  
Kun Yu ◽  
Jie Ma ◽  
Pei An
Keyword(s):  
Image Matching ◽  
Nearest Neighbor ◽  
Feature Matching ◽  
Maximum Clique ◽  
Feature Space ◽  
Multispectral Image ◽  
Feature Descriptor ◽  
Ranking Algorithm ◽  
Feature Based ◽  
Edge Feature

Seeking reliable correspondence between multispectral images is a fundamental and important task in computer vision. To overcome the nonlinearity problem occurring in multispectral image matching, a novel, edge-feature-based maximum clique-matching frame (EMCM) is proposed, which contains three main parts: (1) a novel strong edge binary feature descriptor, (2) a new correspondence-ranking algorithm based on keypoint distinctiveness analysis algorithms in the feature space of the graph, and (3) a false match removal algorithm based on maximum clique searching in the correspondence space of the graph considering both position and angle consistency. Extensive experiments are conducted on two standard multispectral image datasets with respect to the three parts. The feature-matching experiments suggest that the proposed feature descriptor is of high descriptiveness, robustness, and efficiency. The correspondence-ranking experiments validate the superiority of our correspondences-ranking algorithm over the nearest neighbor algorithm, and the coarse registration experiments show the robustness of EMCM with varied interferences.

scholarly journals Hierarchical Aggregation for Reputation Feedback of Services Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Rong Yang ◽  
Dianhua Wang
Keyword(s):  
Matrix Factorization ◽  
State Of The Art ◽  
The State ◽  
Rating Systems ◽  
Aggregation Model ◽  
Aggregate Score ◽  
Hierarchical Aggregation ◽  
Online Marketplaces ◽  
Online Retailers ◽  
Feature Based

Product ratings are popular tools to support buying decisions of consumers, which are also valuable for online retailers. In online marketplaces, vendors can use rating systems to build trust and reputation. To build trust, it is really important to evaluate the aggregate score for an item or a service. An accurate aggregation of ratings can embody the true quality of offerings, which is not only beneficial for providers in adjusting operation and sales tactics, but also helpful for consumers in discovery and purchase decisions. In this paper, we propose a hierarchical aggregation model for reputation feedback, where the state-of-the-art feature-based matrix factorization models are used. We first present our motivation. Then, we propose feature-based matrix factorization models. Finally, we address how to utilize the above modes to formulate the hierarchical aggregation model. Through a set of experiments, we can get that the aggregate score calculated by our model is greater than the corresponding value obtained by the state-of-the-art IRURe; i.e., the outputs of our models can better match the true rank orders.

scholarly journals Feature-Based Attention Elicits Surround Suppression in Feature Space

2014 ◽  
Vol 24 (17) ◽  
pp. 1985-1988 ◽  
Author(s):  
Viola S. Störmer ◽  
George A. Alvarez
Keyword(s):  
Feature Space ◽  
Surround Suppression ◽  
Feature Based

scholarly journals Rapid discovery of novel prophages using biological feature engineering and machine learning

2020 ◽  
Author(s):  
Kimmo Sirén ◽  
Andrew Millard ◽  
Bent Petersen ◽  
M Thomas P Gilbert ◽  
Martha RJ Clokie ◽  
...  
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Feature Space ◽  
Machine Learning Method ◽  
Biological Feature ◽  
Bacterial Genomes ◽  
Current State ◽  
Starting Point ◽  
Feature Based

ABSTRACTProphages are phages that are integrated into bacterial genomes and which are key to understanding many aspects of bacterial biology. Their extreme diversity means they are challenging to detect using sequence similarity, yet this remains the paradigm and thus many phages remain unidentified. We present a novel, fast and generalizing machine learning method based on feature space to facilitate novel prophage discovery. To validate the approach, we reanalyzed publicly available marine viromes and single-cell genomes using our feature-based approaches and found consistently more phages than were detected using current state-of-the-art tools while being notably faster. This demonstrates that our approach significantly enhances bacteriophage discovery and thus provides a new starting point for exploring new biologies.

Somatic Cells Recognition by Application of Gabor Feature-Based (2D)2PCA

2017 ◽  
Vol 31 (12) ◽  
pp. 1757009 ◽  
Author(s):  
Xiaojing Gao ◽  
Heru Xue ◽  
Xin Pan ◽  
Xinhua Jiang ◽  
Yanqing Zhou ◽  
...  
Keyword(s):  
Gabor Filter ◽  
Somatic Cells ◽  
Bovine Mastitis ◽  
Principal Component ◽  
Feature Space ◽  
Support Vector ◽  
Large Set ◽  
Novel Approach ◽  
Gabor Feature ◽  
Feature Based

In this paper, we propose a novel approach of Gabor feature based on bi-directional two-dimensional principal component analysis ((2D)2PCA) for somatic cells recognition. Firstly, Gabor features of different orientations and scales are extracted by the convolution of Gabor filter bank. Secondly, dimensionality reduction of the feature space applies (2D)2PCA in both row and column. Finally, the classifier uses Support Vector Machine (SVM) to achieve our goal. The experimental results are obtained using a large set of images from different sources. The results of our proposed method are not only efficient in accuracy and speed, but also robust to illumination in bovine mastitis via optical microscopy.

scholarly journals Feature Selection for Ridge Regression with Provable Guarantees

2016 ◽  
Vol 28 (4) ◽  
pp. 716-742 ◽  
Author(s):  
Saurabh Paul ◽  
Petros Drineas
Keyword(s):  
Feature Selection ◽  
Ridge Regression ◽  
Feature Selection Method ◽  
Feature Space ◽  
Data Sets ◽  
Real World Data ◽  
Feature Selection Technique ◽  
Worst Case ◽  
Single Set ◽  
Classification Function

We introduce single-set spectral sparsification as a deterministic sampling–based feature selection technique for regularized least-squares classification, which is the classification analog to ridge regression. The method is unsupervised and gives worst-case guarantees of the generalization power of the classification function after feature selection with respect to the classification function obtained using all features. We also introduce leverage-score sampling as an unsupervised randomized feature selection method for ridge regression. We provide risk bounds for both single-set spectral sparsification and leverage-score sampling on ridge regression in the fixed design setting and show that the risk in the sampled space is comparable to the risk in the full-feature space. We perform experiments on synthetic and real-world data sets; a subset of TechTC-300 data sets, to support our theory. Experimental results indicate that the proposed methods perform better than the existing feature selection methods.

Feature-Based Dynamic Pricing

2016 ◽  
Author(s):  
Maxime Cohen ◽  
Ilan Lobel ◽  
Renato Paes Leme
Keyword(s):  
Dynamic Pricing ◽  
Feature Based

scholarly journals Sleep Stage Classification Using Unsupervised Feature Learning

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Martin Längkvist ◽  
Lars Karlsson ◽  
Amy Loutfi
Keyword(s):  
Sleep Stage ◽  
Feature Learning ◽  
Feature Space ◽  
Unsupervised Feature Learning ◽  
Deep Architecture ◽  
Extraction Step ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Selection Step ◽  
Feature Based

Most attempts at training computers for the difficult and time-consuming task of sleep stage classification involve a feature extraction step. Due to the complexity of multimodal sleep data, the size of the feature space can grow to the extent that it is also necessary to include a feature selection step. In this paper, we propose the use of an unsupervised feature learning architecture called deep belief nets (DBNs) and show how to apply it to sleep data in order to eliminate the use of handmade features. Using a postprocessing step of hidden Markov model (HMM) to accurately capture sleep stage switching, we compare our results to a feature-based approach. A study of anomaly detection with the application to home environment data collection is also presented. The results using raw data with a deep architecture, such as the DBN, were comparable to a feature-based approach when validated on clinical datasets.

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