Yada Zhu

Co-Clustering Structural Temporal Data with Applications to Semiconductor Manufacturing

Recent years have witnessed data explosion in semiconductor manufacturing due to advances in instrumentation and storage techniques. In particular, following the same recipe for a certain IC device, multiple tools and chambers can be deployed for the production of this device, during which multiple time series can be collected, such as temperature, impedance, gas flow, electric bias, etc. These time series naturally fit into a two-dimensional array (matrix), i.e., Each element in this array corresponds to a time series for one process variable from one chamber. To leverage the rich structural information in such temporal data, in this paper, we propose a novel framework named C-Struts to simultaneously cluster on the two dimensions of this array. In this framework, we interpret the structural information as a set of constraints on the cluster membership, introduce an auxiliary probability distribution accordingly, and design an iterative algorithm to assign each time series to a certain cluster on each dimension. To the best of our knowledge, we are the first to address this problem. Extensive experiments on benchmark and manufacturing data sets demonstrate the effectiveness of the proposed method.

Parametric Estimation for Window Censored Recurrence Data

Many applications, in particular the failure, repair, and replacement of industrial components or physical infrastructure, involve recurrent events. Frequently, the available data are window-censored: only events that occurred during a particular interval are recorded. Window censoring presents a challenge for recurrence data analysis. For statistical inference from window censored recurrence data, we derive the likelihood function for a model in which the distributions of inter-recurrence intervals in a single path need not be identical and may be associated with covariate information. We assume independence among different sample paths. We propose a distribution to model the effect of external interventions on recurrence processes. This distribution can represent a phenomenon, frequently observed in practice, that the probability of process regeneration increases with the number of historical interventions; for example, an item that had a given number of repairs is generally more likely to be replaced in the wake of a failure than a similar item with a smaller number of repairs. The proposed model and estimation procedure are evaluated via simulation studies and applied to a set of data related to failure and maintenance of water mains. This article has online supplementary material.

Heterogeneous representation learning with separable structured sparsity regularization

Motivated by real applications, heterogeneous learning has emerged as an important research area, which aims to model the coexistence of multiple types of heterogeneity. In this paper, we propose a heterogeneous representation learning model with structured sparsity regularization (HERES) to learn from multiple types of heterogeneity. It aims to leverage the rich correlations (e.g., task relatedness, view consistency, and label correlation) and the prior knowledge (e.g., the soft-clustering of tasks) of heterogeneous data to improve learning performance. To this end, HERES integrates multi-task, multi-view, and multi-label learning into a principled framework based on representation learning to model the complex correlations and employs the structured sparsity to encode the prior knowledge of data. The objective is to simultaneously minimize the reconstruction loss of using the factor matrices to recover the heterogeneous data, and the structured sparsity imposed on the model. The resulting optimization problem is challenging due to the non-smoothness and non-separability of structured sparsity. We reformulate the problem by using the auxiliary function and prove that the reformulation is separable, which leads to an efficient algorithm family for solving structured sparsity penalized problems. Furthermore, we propose various HERES models based on different loss functions and subsume them into the weighted HERES, which is able to handle missing data. The experimental results in comparison with state-of-the-art methods demonstrate the effectiveness of the proposed approach.

E-tail Product Return Prediction via Hypergraph-based Local Graph Cut

Recent decades have witnessed the rapid growth of E-commerce. In particular, E-tail has provided customers with great convenience by allowing them to purchase retail products anywhere without visiting the actual stores. A recent trend in E-tail is to allow free shipping and hassle-free returns to further attract online customers. However, a downside of such a customer-friendly policy is the rapidly increasing return rate as well as the associated costs of handling returned online orders. Therefore, it has become imperative to take proactive measures for reducing the return rate and the associated cost. Despite the large amount of data available from historical purchase and return records, up until now, the problem of E-tail product return prediction has not attracted much attention from the data mining community. To address this problem, in this paper, we propose a generic framework for E-tail product return prediction named HyperGo . It aims to predict the customers intention to return after s/he has put together the shopping basket. For the baskets with a high return intention, the E-tailers can then take appropriate measures to incentivize the customer not to issue a return and/or prepare for reverse logistics. The proposed HyperGo is based on a novel hypergraph representation of historical purchase and return records, effectively leveraging the rich information of basket composition. For a given basket, we propose a local graph cut algorithm using truncated random walk on the hypergraph to identify similar historical baskets. Based on these baskets, HyperGo is able to estimate the return intention on two levels: basket-level vs. product-level, which provides the E-tailers with detailed information regarding the reason for a potential return (e.g., duplicate products with different colors). One major benefit of the proposed local algorithm lies in its time complexity, which is linearly dependent on the size of the output cluster and polylogarithmically dependent on the volume of the hypergraph. This makes HyperGo particularly suitable for processing large-scale data sets. The experimental results on multiple real-world E-tail data sets demonstrate the effectiveness and efficiency of HyperGo .

HiMuV: Hierarchical Framework for Modeling Multi-modality Multi-resolution Data

Many real-world applications are characterized by temporal data collected from multiple modalities, each sampled with a different resolution. Examples include manufacturing processes and financial market prediction. In these applications, an interesting observation is that within the same modality, we often have data from multiple views, thus naturally forming a 2-level hierarchy: with the multiple modalities on the top, and the multiple views at the bottom. For example, in aluminum smelting processes, the multiple modalities include power, noise, alumina feed, etc; and within the same modality such as power, the different views correspond to various voltage, current and resistance control signals and measured responses. For such applications, we aim to address the following challenge, i.e., how can we integrate such multi-modality multi-resolution data to effectively predict the targets of interest, such as bath temperature in aluminum smelting cell and the volatility in financial market. In this paper, for the first time, we simultaneously model the hierarchical data structure and the multi-resolution property via a novel framework named HiMuV. Different from existing work based on multiple views on a single level or a single resolution, the proposed framework is based on the key assumption that the information from different modalities is complementary, whereas the information within the same modality (across different views) is redundant in terms of predicting the targets of interest. Therefore, we introduce an optimization framework where the objective function contains both the prediction loss and a novel regularizer enforcing the consistency among different views within the same modality. To solve this optimization framework, we propose an iterative algorithm based on randomized block coordinate descent. Experimental results on synthetic data, benchmark data, and various real data sets from aluminum smelting processes, and stock market prediction demonstrate the effectiveness and efficiency of the proposed algorithm.