Multi-Label Learning with Label Enhancement

The task of multi-label learning is to predict a set of relevant labels for the unseen instance. Traditional multi-label learning algorithms treat each class label as a logical indicator of whether the corresponding label is relevant or irrelevant to the instance, i.e., +1 represents relevant to the instance and -1 represents irrelevant to the instance. Such label represented by -1 or +1 is called logical label. Logical label cannot reflect different label importance. However, for real-world multi-label learning problems, the importance of each possible label is generally different. For the real applications, it is difficult to obtain the label importance information directly. Thus we need a method to reconstruct the essential label importance from the logical multilabel data. To solve this problem, we assume that each multi-label instance is described by a vector of latent real-valued labels, which can reflect the importance of the corresponding labels. Such label is called numerical label. The process of reconstructing the numerical labels from the logical multi-label data via utilizing the logical label information and the topological structure in the feature space is called Label Enhancement. In this paper, we propose a novel multi-label learning framework called LEMLL, i.e., Label Enhanced Multi-Label Learning, which incorporates regression of the numerical labels and label enhancement into a unified framework. Extensive comparative studies validate that the performance of multi-label learning can be improved significantly with label enhancement and LEMLL can effectively reconstruct latent label importance information from logical multi-label data.Comment: ICDM 201

Decomposition-based Generation Process for Instance-Dependent Partial Label Learning

Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true. Most existing PLL approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels and model the generation process of the candidate labels in a simple way. However, these approaches usually do not perform as well as expected due to the fact that the generation process of the candidate labels is always instance-dependent. Therefore, it deserves to be modeled in a refined way. In this paper, we consider instance-dependent PLL and assume that the generation process of the candidate labels could decompose into two sequential parts, where the correct label emerges first in the mind of the annotator but then the incorrect labels related to the feature are also selected with the correct label as candidate labels due to uncertainty of labeling. Motivated by this consideration, we propose a novel PLL method that performs Maximum A Posterior(MAP) based on an explicitly modeled generation process of candidate labels via decomposed probability distribution models. Experiments on benchmark and real-world datasets validate the effectiveness of the proposed method

Tree growth acceleration and expansion of alpine forests: The synergistic effect of atmospheric and edaphic change.

Many forest ecosystems have experienced recent declines in productivity; however, in some alpine regions, tree growth and forest expansion are increasing at marked rates. Dendrochronological analyses at the upper limit of alpine forests in the Tibetan Plateau show a steady increase in tree growth since the early 1900s, which intensified during the 1930s and 1960s, and have reached unprecedented levels since 1760. This recent growth acceleration was observed in small/young and large/old trees and coincided with the establishment of trees outside the forest range, reflecting a connection between the physiological performance of dominant species and shifts in forest distribution. Measurements of stable isotopes (carbon, oxygen, and nitrogen) in tree rings indicate that tree growth has been stimulated by the synergistic effect of rising atmospheric CO2 and a warming-induced increase in water and nutrient availability from thawing permafrost. These findings illustrate the importance of considering soil-plant-atmosphere interactions to understand current and anticipate future changes in productivity and distribution of forest ecosystems

Can Class-Priors Help Single-Positive Multi-Label Learning?

Single-positive multi-label learning (SPMLL) is a typical weakly supervised multi-label learning problem, where each training example is annotated with only one positive label. Existing SPMLL methods typically assign pseudo-labels to unannotated labels with the assumption that prior probabilities of all classes are identical. However, the class-prior of each category may differ significantly in real-world scenarios, which makes the predictive model not perform as well as expected due to the unrealistic assumption on real-world application. To alleviate this issue, a novel framework named {\proposed}, i.e., Class-pRiors Induced Single-Positive multi-label learning, is proposed. Specifically, a class-priors estimator is introduced, which could estimate the class-priors that are theoretically guaranteed to converge to the ground-truth class-priors. In addition, based on the estimated class-priors, an unbiased risk estimator for classification is derived, and the corresponding risk minimizer could be guaranteed to approximately converge to the optimal risk minimizer on fully supervised data. Experimental results on ten MLL benchmark datasets demonstrate the effectiveness and superiority of our method over existing SPMLL approaches

New core-shell microgel offers creative texture and water-fresh sensory qualities to skin care products.

Sensorial attributes such as texture are essential to designing skin care products that satisfy growing consumer trends. Momentive’s patented, silicone-grafted polyacrylate microgel is an innovative, multifunctional structuring/texturing agent that can deliver a wide range of new, creative textures with a water-fresh feel. The swelling and emulsification properties of this multifunctional hydrophilic/hydrophobic particle demonstrate the microgel’s versatility and array of achievable textures. Core-shell microgel can emulsify a large volume of oil and provide excellent dispersion power for solid particles, and an enhanced, lightweight feel to sunscreen formulations. Silicone copolymer grafting can allow the development of skin care formulations that achieve more with less—more functionality, fewer ingredients