Jayanta Basak

Cross-Channel Customer Mapping

In multi-channel setting (for example, Web channel and storefront), one often is required to generate an integrated view of customers across channels for making better CRM, marketing and merchandizing decisions. It is essential to identify a customer uniquely across channels to generate an integrated customer view. It is often not feasible to impose a unique identifier on a customer across channels. Moreover, a customer may not provide true demographic information, making it even more difficult to track. In the absence of a unique identifier and correct demographic information, the behavioral signature of a customer can perhaps be used to track customer across channels (cross-channel customer mapping) where behavioral signature comprises of the channel-independent behavioral characteristics. We define certain channel-independent behavioral characteristics that are easily computable and adaptable with incremental information gain. We then provide algorithms to match behavioral signatures across channels. We demonstrate our methodology using safeway data, where we achieved significant accuracy, for example, over 90% for the high value customers.

Methods of case adaptation: A survey

In this article, we provide an overview of the case adaptation process. We classify various existing case adaptation methods available in the literature. We consider three different aspects, namely, domain knowledge requirement, adaptive capabilities of the case adaptation methods, and the kind of adaptation knowledge required. We then derive certain findings about the nature of the case adaptation methods and their applicability in real‐life tasks.

Methods of case adaptation: A survey: Research Articles

A model of an extended fuzzy relational database was proposed to accommodate uncertain and imprecise information. We use two supplementary measurements, satisfactory degree and extra degree, for determining the quality of answers to Select-Project-Join (SPJ) queries. The method of measurement determines how much satisfactory information is provided and how much truth information is required for a query. The answers to the query thus contain sure answers and maybe answers. The core of this study is the detailed discussion on the quality of answers in an extended fuzzy relation to query processing.

Multiple Exemplar-Based Facial Image Retrieval Using Independent Component Analysis

In this paper, we design a content-based image retrieval system where multiple query examples can be used to indicate the need to retrieve not only images similar to the individual examples, but also those images which actually represent a combination of the content of query images. We propose a scheme for representing content of an image as a combination of features from multiple examples. This scheme is exploited for developing a multiple example-based retrieval engine. We have explored the use of machine learning techniques for generating the most appropriate feature combination scheme for a given class of images. The combination scheme can be used for developing purposive query engines for specialized image databases. Here, we have considered facial image databases. The effectiveness of the image retrieval system is experimentally demonstrated on different databases

A least square kernel machine with box constraints

In this paper, we present a least square kernel machine with box constraints (LSKMBC). The existing least square machines assume Gaussian hyperpriors and subsequently express the optima of the regularized squared loss as a set of linear equations. The generalized LASSO framework deviates from the assumption of Gaussian hyperpriors and employs a more general Huber loss function. In our approach, we consider uniform priors and obtain the loss functional for a given margin considered to be a model selection parameter. The framework not only differs from the existing least square kernel machines, but also it does not require Mercer condition satisfiability. Experimentally we validate the performance of the classifier and show that it is able to outperform SVM and LSSVM on certain real-life datasets.

Video summarization with supervised learning

We present a video summarization technique based on supervised learning. Within a class of videos of similar nature, user provides the desired summaries for a subset of videos. Based on this supervised information, the summaries for other videos in the same class are generated. We derive frame-transitional features and subsequently represent each frame transition as a state. We then formulate a loss functional to quantify the discrepency between state transitional probabilities in the original video and that in the intended summary video, and optimize this functional. We experimentally validate the performance of the technique using cross-validation scores on two different class of videos, and demonstrate that the proposed technique is able to produce high quality summarization capturing the user perception.

QPLC: A novel multimodal biometric score fusion method

In biometrics authentication systems, it has been shown that fusion of more than one modality (e.g., face and finger) and fusion of more than one classifier (two different algorithms) can improve the system performance. Often a score level fusion is adopted as this approach doesnt require the vendors to reveal much about their algorithms and features. Many score level transformations have been proposed in the literature to normalize the scores which enable fusion of more than one classifier. In this paper, we propose a novel score level transformation technique that helps in fusion of multiple classifiers. The method is based on two components: quantile transform of the genuine and impostor score distributions and a power transform which further changes the score distribution to help linear classification. After the scores are normalized using the novel quantile power transform, several linear classifiers are proposed to fuse the scores of multiple classifiers. Using the NIST BSSR-1 dataset, we have shown that the results obtained by the proposed method far exceed the results published so far in the literature.

A Gradient Descent Approach for Multi-modal Biometric Identification

While biometrics-based identification is a key technology in many critical applications such as searching for an identity in a watch list or checking for duplicates in a citizen ID card system, there are many technical challenges in building a solution because the size of the database can be very large (often in 100s of millions) and the intrinsic errors with the underlying biometrics engines. Often multi-modal biometrics is proposed as a way to improve the underlying biometrics accuracy performance. In this paper, we propose a score based fusion scheme tailored for identification applications. The proposed algorithm uses a gradient descent method to learn weights for each modality such that weighted sum of genuine scores is larger than the weighted sum of all the impostor scores. During the identification phase, top K candidates from each modality are retrieved and a super-set of identities is constructed. Using the learnt weights, we compute the weighted score for all the candidates in the superset. The highest scoring candidate is declared as the top candidate for identification. The proposed algorithm has been tested using NIST BSSR-1 dataset and results in terms of accuracy as well as the speed (execution time) are shown to be far superior than the published results on this dataset.

Online adaptive clustering in a decision tree framework

We present an online adaptive clustering algorithm in a decision tree framework which has an adaptive tree and a code formation layer. The code formation layer stores the representative codes of the clusters and the tree adapts the separating hyperplanes between the clusters. The membership of a sample in a cluster is decided by the tree and the tree parameters are guided by stored codes. The model provides a hierarchical representation of the clusters by minimizing a global objective function as opposed to the existing hierarchical clusterings where a local objective function at every level is optimized. We show the results on real-life data.

Detection of Neural Activities in FMRI Using Jensen-Shannon Divergence

In this paper, we present a statistical technique based on Jensen-Shanon divergence for detecting the regions of activity in fMRI images. The method is model free and we exploit the metric property of the square root of Jensen-Shannon divergence to accumulate the variations between successive time frames of fMRI images. Experimentally we show the effectiveness of our algorithm.