On the Robustness of Average Losses for Partial-Label Learning

Area of Science:

• Machine Learning
• Artificial Intelligence
• Data Science

Background:

• Partial-label learning (PLL) involves instances with partial labels (PLs), where each PL contains multiple candidate labels, but only one is the true label (TL).
• Existing PLL research predominantly focuses on identification-based strategies (IBS) for purifying PLs and selecting the TL.
• Average-based strategies (ABS) treat all candidate labels equally, offering an alternative approach to PLL.

Purpose of the Study:

• To analyze the limitations of average-based strategies (ABS) in partial-label learning (PLL).
• To propose theoretical and experimental improvements for ABS in PLL.
• To investigate the robustness of average partial-label losses (APLLs) and their impact on PLL performance.

Main Methods:

• Theoretical analysis of PLL robustness by proposing two problem settings and proving properties of APLLs with bounded and unbounded losses.
• Experimental validation of ABS using bounded losses against state-of-the-art IBS.
• Investigating the effectiveness of using robust APLLs for warm-starting IBS.

Main Results:

• The first theoretical robustness analysis for PLL demonstrates that APLLs with bounded losses are robust, while those with unbounded losses may not be.
• ABS utilizing bounded losses achieves performance comparable to or exceeding state-of-the-art IBS employing unbounded losses.
• Warm-starting IBS with robust APLLs leads to further performance improvements for the IBS itself.

Conclusions:

• Average-based strategies (ABS) are a viable and potentially superior alternative in partial-label learning (PLL) when appropriately formulated.
• Robust average partial-label losses (APLLs) are crucial for effective ABS and can enhance identification-based strategies (IBS).
• This research encourages further exploration of ABS, potentially advancing the entire field of partial-label learning.