Latent feature models (LFM)s are widely employed for extracting latent structures of data. While offering high, parameter estimation is difficult with LFMs because of the combinational nature of latent features, and non-identifiability is a particularly difficult problem when parameter estimation is not unique and there exists equivalent solutions. In this paper, a necessary and sufficient condition for non-identifiability is shown. The condition is significantly related to dependency of features, and this implies that non-identifiability may often occur in real-world applications. A novel method for parameter estimation that solves the non-identifiability problem is also proposed. This method can be combined as a post-process with existing methods and can find an appropriate solution by hopping efficiently through equivalent solutions. We have evaluated the effectiveness of the method on both synthetic and real-world datasets.

Abstract--Robust PCA, the problem of PCA in the presence of outliers has been extensively investigated in the last few years. Here we focus on Robust PCA in the outlier model where each column of the data matrix is either an inlier or an outlier. Most of the existing methods for this model assumes either the knowledge of the dimension of the lower dimensional subspace or the fraction of outliers in the system. However in many applications knowledge of these parameters is not available. Motivated by this we propose a parameter free outlier identification method for robust PCA which a) does not require the knowledge of outlier fraction, b) does not require the knowledge of the dimension of the underlying subspace, c) is computationally simple and fast d) can handle structured and unstructured outliers. Further, analytical guarantees are derived for outlier identification and the performance of the algorithm is compared with the existing state of the art methods in both real and synthetic data for various outlier structures. Principal Component Analysis (PCA) [1] is a very widely used technique in data analysis and dimensionality reduction. Singular Value Decomposition (SVD) of the data matrix M [2] is known to be very sensitive to extreme corruptions in the data [3], [4], [5] and hence robustifying the PCA process becomes a necessity. Robust PCA is typically an ill posed problem and it is of significant importance in a wide variety of fields like computer vision, machine learning, survey data analysis and so on. Of the numerous approaches to robust PCA over the years [8], [9], one way to model extreme corruptions in the given data matrix M, is using the following decomposition [10], [11], [12], [3]: M L S, where S encapsulates all the corruptions and is assumed to be sparse and L is low rank.

In graphs with rich text information, constructing expressive graph representations requires incorporating textual information with structural information. Graph embedding models are becoming more and more popular in representing graphs, yet they are faced with two issues: sampling efficiency and text utilization. Through analyzing existing models, we find their training objectives are composed of pairwise proximities, and there are large amounts of redundant node pairs in Random Walk-based methods. Besides, inferring graph structures directly from texts (also known as zero-shot scenario) is a problem that requires higher text utilization. To solve these problems, we propose a novel Text-driven Graph Embedding with Pairs Sampling (TGE-PS) framework. TGE-PS uses Pairs Sampling (PS) to generate training samples which reduces ~99% training samples and is competitive compared to Random Walk. TGE-PS uses Text-driven Graph Embedding (TGE) which adopts word- and character-level embeddings to generate node embeddings. We evaluate TGE-PS on several real-world datasets, and experimental results demonstrate that TGE-PS produces state-of-the-art results in traditional and zero-shot link prediction tasks.

Robot manipulation is increasingly poised to interact with humans in co-shared workspaces. Despite increasingly robust manipulation and control algorithms, failure modes continue to exist whenever models do not capture the dynamics of the unstructured environment. To obtain longer-term horizons in robot automation, robots must develop introspection and recovery abilities. We contribute a set of recovery policies to deal with anomalies produced by external disturbances as well as anomaly classification through the use of non-parametric statistics with memoized variational inference with scalable adaptation. A recovery critic stands atop of a tightly-integrated, graph-based online motion-generation and introspection system that resolves a wide range of anomalous situations. Policies, skills, and introspection models are learned incrementally and contextually in a task. Two task-level recovery policies: re-enactment and adaptation resolve accidental and persistent anomalies respectively. The introspection system uses non-parametric priors along with Markov jump linear systems and memoized variational inference with scalable adaptation to learn a model from the data. Extensive real-robot experimentation with various strenuous anomalous conditions is induced and resolved at different phases of a task and in different combinations. The system executes around-the-clock introspection and recovery and even elicited self-recovery when misclassifications occurred.

This paper aims at providing insight on the transferability of deep CNN features to unsupervised problems. We study the impact of different pretrained CNN feature extractors on the problem of image set clustering for object classification as well as fine-grained classification. We propose a rather straightforward pipeline combining deep-feature extraction using a CNN pretrained on ImageNet and a classic clustering algorithm to classify sets of images. This approach is compared to state-of-the-art algorithms in image-clustering and provides better results. These results strengthen the belief that supervised training of deep CNN on large datasets, with a large variability of classes, extracts better features than most carefully designed engineering approaches, even for unsupervised tasks. We also validate our approach on a robotic application, consisting in sorting and storing objects smartly based on clustering.

Doctors often rely on their past experience in order to diagnose patients. For a doctor with enough experience, almost every patient would have similarities to key cases seen in the past, and each new patient could be viewed as a mixture of these key past cases. Because doctors often tend to reason this way, an efficient computationally aided diagnostic tool that thinks in the same way might be helpful in locating key past cases of interest that could assist with diagnosis. This article develops a novel mathematical model to mimic the type of logical thinking that physicians use when considering past cases. The proposed model can also provide physicians with explanations that would be similar to the way they would naturally reason about cases. The proposed method is designed to yield predictive accuracy, computational efficiency, and insight into medical data; the key element is the insight into medical data - in some sense we are automating a complicated process that physicians might perform manually. We finally implemented the result of this work on two publicly available healthcare datasets, for (1) heart disease prediction and (2) breast cancer prediction.

A system of nested dichotomies is a method of decomposing a multi-class problem into a collection of binary problems. Such a system recursively applies binary splits to divide the set of classes into two subsets, and trains a binary classifier for each split. Many methods have been proposed to perform this split, each with various advantages and disadvantages. In this paper, we present a simple, general method for improving the predictive performance of nested dichotomies produced by any subset selection techniques that employ randomness to construct the subsets. We provide a theoretical expectation for performance improvements, as well as empirical results showing that our method improves the root mean squared error of nested dichotomies, regardless of whether they are employed as an individual model or in an ensemble setting.

Although modern recommendation systems can exploit the structure in users' item feedback, most are powerless in the face of new users who provide no structure for them to exploit. In this paper we introduce ImplicitCE, an algorithm for recommending items to new users during their sign-up flow. ImplicitCE works by transforming users' implicit feedback towards auxiliary domain items into an embedding in the target domain item embedding space. ImplicitCE learns these embedding spaces and transformation function in an end-to-end fashion and can co-embed users and items with any differentiable similarity function. To train ImplicitCE we explore methods for maximizing the correlations between model predictions and users' affinities and introduce Sample Correlation Update, a novel and extremely simple training strategy. Finally, we show that ImplicitCE trained with Sample Correlation Update outperforms a variety of state of the art algorithms and loss functions on both a large scale Twitter dataset and the DBLP dataset.

In this paper, we consider the problem of distinguishing between authorized and unauthorized touchscreen and smart phone device users by leveraging a learned gesture classification profile combined with gesture anomaly detection. As touchscreen devices become more ubiquitous and the information stored within them becomes increasingly personal and valuable, the incentive and reward for circumventing existing security mechanisms has increased substantially. Given known vulnerabilities in existing biometric and non-biometric authentication methods such as fingerprint scanners, facial recognition, tokens, and pass codes; the development of an effective authentication approach that goes beyond the'something you know / have / are' paradigm is needed. In this paper, we propose models that accurately predict users based on touchscreen gesture patterns and detect anomalies in these patterns as a versatile approach to augment existing security methods and provide a method of continuous authentication. Touchscreen gesture are collected from a set of users from a capacitive sensor array to simulate a smart phone. Features include the pressure measured at the two dimensional (X,Y) coordinates on the sensor for each gesture, velocity at different instances of the gesture, and the duration of the gesture. We then demonstrate how logistic regression, support vector machines (SVM), and multiple Gaussian processes can be used to classify and predict the user creating the gesture. Our intent is to determine the extent to which supervised and unsupervised learning approaches can be successfully leveraged across multiple domains to limit the impact of unauthorized touchscreen device usage, quantify touchscreen security weaknesses and vulnerabilities, and potentially inform touchscreen device security design. Scenarios where our analysis may be useful include high security use cases where continuous authentication is required in the finance, transportation, public safety sectors.

Symbolic data analysis (SDA) is an emerging area of statistics based on aggregating individual level data into group-based distributional summaries (symbols), and then developing statistical methods to analyse them. It is ideal for analysing large and complex datasets, and has immense potential to become a standard inferential technique in the near future. However, existing SDA techniques are either non-inferential, do not easily permit meaningful statistical models, are unable to distinguish between competing models, and are based on simplifying assumptions that are known to be false. Further, the procedure for constructing symbols from the underlying data is erroneously not considered relevant to the resulting statistical analysis. In this paper we introduce a new general method for constructing likelihood functions for symbolic data based on a desired probability model for the underlying classical data, while only observing the distributional summaries. This approach resolves many of the conceptual and practical issues with current SDA methods, opens the door for new classes of symbol design and construction, in addition to developing SDA as a viable tool to enable and improve upon classical data analyses, particularly for very large and complex datasets. This work creates a new direction for SDA research, which we illustrate through several real and simulated data analyses.