Semi-supervised novelty detection

A common setting for novelty detection assumes that labeled examples from the nominal class are available, but that labeled examples of novelties are unavailable. The standard (inductive) approach is to declare novelties where the nominal density is low, which reduces the problem to density level set estimation. In this paper, we consider the setting where an unlabeled and possibly contaminated sample is also available at learning time. We argue that novelty detection in this semi-supervised setting is naturally solved by a general reduction to a binary classification problem. In particular, a detector with a desired false positive rate can be achieved through a reduction to Neyman-Pearson classification. Unlike the inductive approach, semi-supervised novelty detection (SSND) yields detectors that are optimal (e.g., statistically consistent) regardless of the distribution on novelties. Therefore, in novelty detection, unlabeled data have a substantial impact on the theoretical properties of the decision rule. We validate the practical utility of SSND with an extensive experimental study. We also show that SSND provides distribution-free, learning-theoretic solutions to two well known problems in hypothesis testing. First, our results provide a general solution to the general two-sample problem, that is, the problem of determining whether two random samples arise from the same distribution. Second, a specialization of SSND coincides with the standard $p$-value approach to multiple testing under the so-called random effects model. Unlike standard rejection regions based on thresholded $p$-values, the general SSND framework allows for adaptation to arbitrary alternative distributions

Semi-Supervised Eigenbasis Novelty Detection

Recent discoveries in high-time-resolution radio astronomy data have focused attention on a new class of events. Fast transients are rare pulses of radio frequency energy lasting from microseconds to seconds that might be produced by a variety of exotic astrophysical phenomena. For example, X-ray bursts, neutron stars, and active galactic nuclei are all possible sources of short-duration, transient radio signals. It is difficult to anticipate where such signals might appear, and they are most commonly discovered through analysis of high-time- resolution data that had been collected for other purposes. Transients are often faint and difficult to detect, so improved detection algorithms can directly benefit the science yield of all such commensal monitoring. A new detection algorithm learns a low-dimensional linear manifold for describing the normal data. High reconstruction error indicates a novel signal that does not match the patterns of normal data. One unsupervised portion of the manifold model adapts its representation in response to recent data. A second supervised portion of the model is made of a basis trained in advance using labeled examples of RFI; this prevents false positives due to these events. For a linear model, an orthonormalization operation is used to combine these bases prior to the anomaly detection decision. Another novel aspect of the approach lies in combining basis vectors learned in an unsupervised, online fashion from the data stream with supervised basis vectors learned in advance from known examples of false alarms. Adaptive, data-driven detection is achieved that is also informed by existing domain knowledge about signals that may be statistically anomalous, but are not interesting and should therefore be ignored. The method was evaluated using data from the Parkes Multibeam Survey. This data set was originally collected to search for pulsars, which are astronomical sources that emit radio pulses at regular periods. However, several non-pulsar anomalies have recently been discovered in this dataset, making it a compelling test case. By explicitly filtering known false alarm patterns, the approach yields significantly better performance than current transient detection methods

Generative Models for Novelty Detection Applications in abnormal event and situational changedetection from data series

Novelty detection is a process for distinguishing the observations that differ in some respect from the observations that the model is trained on. Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains observations that were not known at the training time. In other words, the novelty class is often is not presented during the training phase or not well defined. In light of the above, one-class classifiers and generative methods can efficiently model such problems. However, due to the unavailability of data from the novelty class, training an end-to-end model is a challenging task itself. Therefore, detecting the Novel classes in unsupervised and semi-supervised settings is a crucial step in such tasks. In this thesis, we propose several methods to model the novelty detection problem in unsupervised and semi-supervised fashion. The proposed frameworks applied to different related applications of anomaly and outlier detection tasks. The results show the superior of our proposed methods in compare to the baselines and state-of-the-art methods

Semi-Supervised Novelty Detection using SVM entire solution path

Very often, the only reliable information available to perform change detection is the description of some unchanged regions. Since sometimes these regions do not contain all the relevant information to identify their counterpart (the changes), we consider the use of unlabeled data to perform Semi-Supervised Novelty detection (SSND). SSND can be seen as an unbalanced classification problem solved using the Cost-Sensitive Support Vector Machine (CS-SVM), but this requires a heavy parameter search. We propose here to use entire solution path algorithms for the CS-SVM in order to facilitate and accelerate the parameter selection for SSND. Two algorithms are considered and evaluated. The first one is an extension of the CS-SVM algorithm that returns the entire solution path in a single optimization. This way, the optimization of a separate model for each hyperparameter set is avoided. The second forces the solution to be coherent through the solution path, thus producing classification boundaries that are nested (included in each other). We also present a low density criterion for selecting the optimal classification boundaries, thus avoiding the recourse to cross-validation that usually requires information about the ``change'' class. Experiments are performed on two multitemporal change detection datasets (flood and fire detection). Both algorithms tracing the solution path provide similar performances than the standard CS-SVM while being significantly faster. The low density criterion proposed achieves results that are close to the ones obtained by cross-validation, but without using information about the changes

Identification of Unknown Landscape Types Using CNN Transfer Learning

Unknown image type identification is the problem of identifying unknown types of images from the set of already provided images that are considered to be known, where the known and unknown sets represent different content types. Solving this problem has a lot of security applications such as suspicious object detection during baggage scanning at airport customs, border protection via remote sensing, cancer detection, weather and disaster monitoring, etc. In this thesis, we focus on identification of unknown landscape images. This application has a huge relevance to the context of a smart nation where it can be applied to major national security tasks such as monitoring the borders or the detection of unknown and potentially dangerous landscapes in critical locations. We propose effective semi-supervised novelty detection approaches for the unknown image type identification problem using Convolutional Neural Network (CNN) Transfer Learning. Recently, the CNN Transfer Learning approach has been very successful in various visual recognition tasks especially in cases where large training data is not available. Our main idea is to use pre-trained CNNs (i.e. already trained on large datasets like ImageNet [10]) that are then used to train new models specifically applicable to the landscape image dataset. Features extracted from these domain-specific trained CNN are then used with standard semi-supervised novelty detection algorithms like Gaussian Mixture Model, Isolation Forest, One-class Support Vector Machines (SVM) and Bayesian Gaussian Mixture Models to identify the unknown landscape images. We provide two fine-tuning approaches: supervised and unsupervised. Supervised fine-tuning approach simply uses the the class categories (landscape classes, e.g. airport, stadium, etc.) of the known images dataset. The unsupervised fine tuning approach on the other hand learns the class categories from the known images using the unsupervised clustering-based algorithm. We conducted extensive experiments that prove the effectiveness of our approaches. Our best values of AUROC and average precision scores for the identification problem are 0.96 and 0.94, respectively. In particular, we statistically prove that both fine-tuning methods significantly increase the performance of the identification with respect to the non fine-tuned CNN, and unsupervised and supervised fine tuning approaches are comparable

Identifying Restaurants Proposing Novel Kinds of Cuisines: Using Yelp Reviews

These days with TV-shows and starred chefs, new kinds of cuisines appear in the market. The main cuisines like French, Italian, Japanese, Chinese and Indian are always appreciated but they are no longer the most popular. The new trend is the fusion cuisine, which is obtained by combining different main cuisines. The opening of a new restaurant proposing new kinds of cuisine produces a lot of excitement in people. They feel the need to try it and be part of this new culture. Yelp is a platform which publishes crowd sourced reviews about different businesses, in particular, restaurants. For some restaurants in Yelp if the kind of cuisine is available, usually, there is a tag only for the main cuisines, but there is no information for the fusion cuisine. There is a need to develop a system which is able to identify restaurants proposing fusion cuisine (novel or unknown cuisines). This proposal is to address the novelty detection task using Yelp reviews. The idea is that the semi-supervised Machine Learning models trained only on the reviews of restaurants proposing the main cuisine will be able to discriminate between restaurants providing the main cuisine and restaurants providing the novel ones. We propose effective novelty detection approaches for the unknown cuisine type identification problem using Long Short Term Memory (LSTM), autoencoder and Term-Frequency and Inverse Document Frequency(). Our main idea is to obtain features from LSTM, autoencoder and TF-IDF and use these features with standard semi-supervised novelty detection algorithms like Gaussian Mixture Model, Isolation Forest and One-class Support Vector Machines (SVM) to identify the unknown cuisines. We conducted extensive experiments that prove the effectiveness of our approaches. The score that we obtained has a very high discrimination power because the best value of AUROC for the novelty detection problem is 0.85 from LSTM. LSTM outperforms our baseline model of TF-IDF and the main motivation is due to its ability to retain only the useful parts of a sentence

Semi-supervised novelty detection with one class SVM for SMS spam detection

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The volume of SMS messages sent on a daily basis globally has continued to grow significantly over the past years. Hence, mobile phones are becoming increasingly vulnerable to SMS spam messages, thereby exposing users to the risk of fraud and theft of personal data. Filtering of messages to detect and eliminate SMS spam is now a critical functionality for which different types of machine learning approaches are still being explored. In this paper, we propose a system for detecting SMS spam using a semi-supervised novelty detection approach based on one class SVM classifier. The system is built as an anomaly detector that learns only from normal SMS messages thus enabling detection models to be implemented in the absence of labelled SMS spam training examples. We evaluated our proposed system using a benchmark dataset consisting of 747 SMS spam and 4827 non-spam messages. The results show that our proposed method outperformed the traditional supervised machine learning approaches based on binary, frequency or TF-IDF bag-of-words. The overall accuracy was 98% with 100% SMS spam detection rate and only around 3% false positive rate