Online Product Quantization

Approximate nearest neighbor (ANN) search has achieved great success in many tasks. However, existing popular methods for ANN search, such as hashing and quantization methods, are designed for static databases only. They cannot handle well the database with data distribution evolving dynamically, due to the high computational effort for retraining the model based on the new database. In this paper, we address the problem by developing an online product quantization (online PQ) model and incrementally updating the quantization codebook that accommodates to the incoming streaming data. Moreover, to further alleviate the issue of large scale computation for the online PQ update, we design two budget constraints for the model to update partial PQ codebook instead of all. We derive a loss bound which guarantees the performance of our online PQ model. Furthermore, we develop an online PQ model over a sliding window with both data insertion and deletion supported, to reflect the real-time behaviour of the data. The experiments demonstrate that our online PQ model is both time-efficient and effective for ANN search in dynamic large scale databases compared with baseline methods and the idea of partial PQ codebook update further reduces the update cost.Comment: To appear in IEEE Transactions on Knowledge and Data Engineering (DOI: 10.1109/TKDE.2018.2817526

Evolving Ensemble Fuzzy Classifier

The concept of ensemble learning offers a promising avenue in learning from data streams under complex environments because it addresses the bias and variance dilemma better than its single model counterpart and features a reconfigurable structure, which is well suited to the given context. While various extensions of ensemble learning for mining non-stationary data streams can be found in the literature, most of them are crafted under a static base classifier and revisits preceding samples in the sliding window for a retraining step. This feature causes computationally prohibitive complexity and is not flexible enough to cope with rapidly changing environments. Their complexities are often demanding because it involves a large collection of offline classifiers due to the absence of structural complexities reduction mechanisms and lack of an online feature selection mechanism. A novel evolving ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in this paper. pENsemble differs from existing architectures in the fact that it is built upon an evolving classifier from data streams, termed Parsimonious Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism, which estimates a localized generalization error of a base classifier. A dynamic online feature selection scenario is integrated into the pENsemble. This method allows for dynamic selection and deselection of input features on the fly. pENsemble adopts a dynamic ensemble structure to output a final classification decision where it features a novel drift detection scenario to grow the ensemble structure. The efficacy of the pENsemble has been numerically demonstrated through rigorous numerical studies with dynamic and evolving data streams where it delivers the most encouraging performance in attaining a tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System

Warped K-Means: An algorithm to cluster sequentially-distributed data

[EN] Many devices generate large amounts of data that follow some sort of sequentiality, e.g., motion sensors, e-pens, eye trackers, etc. and often these data need to be compressed for classification, storage, and/or retrieval tasks. Traditional clustering algorithms can be used for this purpose, but unfortunately they do not cope with the sequential information implicitly embedded in such data. Thus, we revisit the well-known K-means algorithm and provide a general method to properly cluster sequentially-distributed data. We present Warped K-Means (WKM), a multi-purpose partitional clustering procedure that minimizes the sum of squared error criterion, while imposing a hard sequentiality constraint in the classification step. We illustrate the properties of WKM in three applications, one being the segmentation and classification of human activity. WKM outperformed five state-of- the-art clustering techniques to simplify data trajectories, achieving a recognition accuracy of near 97%, which is an improvement of around 66% over their peers. Moreover, such an improvement came with a reduction in the computational cost of more than one order of magnitude.This work has been partially supported by Casmacat (FP7-ICT-2011-7, Project 287576), tranScriptorium (FP7-ICT-2011-9, Project 600707), STraDA (MINECO, TIN2012-37475-0O2-01), and ALMPR (GVA, Prometeo/20091014) projects.Leiva Torres, LA.; Vidal, E. (2013). Warped K-Means: An algorithm to cluster sequentially-distributed data. Information Sciences. 237:196-210. https://doi.org/10.1016/j.ins.2013.02.042S19621023

Exploratory Cluster Analysis from Ubiquitous Data Streams using Self-Organizing Maps

This thesis addresses the use of Self-Organizing Maps (SOM) for exploratory cluster analysis over ubiquitous data streams, where two complementary problems arise: first, to generate (local) SOM models over potentially unbounded multi-dimensional non-stationary data streams; second, to extrapolate these capabilities to ubiquitous environments. Towards this problematic, original contributions are made in terms of algorithms and methodologies. Two different methods are proposed regarding the first problem. By focusing on visual knowledge discovery, these methods fill an existing gap in the panorama of current methods for cluster analysis over data streams. Moreover, the original SOM capabilities in performing both clustering of observations and features are transposed to data streams, characterizing these contributions as versatile compared to existing methods, which target an individual clustering problem. Also, additional methodologies that tackle the ubiquitous aspect of data streams are proposed in respect to the second problem, allowing distributed and collaborative learning strategies. Experimental evaluations attest the effectiveness of the proposed methods and realworld applications are exemplified, namely regarding electric consumption data, air quality monitoring networks and financial data, motivating their practical use. This research study is the first to clearly address the use of the SOM towards ubiquitous data streams and opens several other research opportunities in the future

Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review

Recent crises, recessions and bubbles have stressed the non-stationary nature and the presence of drastic structural changes in the financial domain. The most recent literature suggests the use of conventional machine learning and statistical approaches in this context. Unfortunately, several of these techniques are unable or slow to adapt to changes in the price-generation process. This study aims to survey the relevant literature on Machine Learning for financial prediction under regime change employing a systematic approach. It reviews key papers with a special emphasis on technical analysis. The study discusses the growing number of contributions that are bridging the gap between two separate communities, one focused on data stream learning and the other on economic research. However, it also makes apparent that we are still in an early stage. The range of machine learning algorithms that have been tested in this domain is very wide, but the results of the study do not suggest that currently there is a specific technique that is clearly dominant